Unprocessed aerial imagery from 2 March 2023 coastal survey of Central California.
This is a set of 1839 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 February 2023 coastal survey of Central California.
This is a set of 1939 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 1 February 2023 coastal survey of Central California.
This is a set of 2943 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 23 January 2023 coastal survey of Central California.
This is a set of 5039 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 16 January 2023 coastal survey of Central California.
This is a set of 2763 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 January 2023 coastal survey of Central California.
This is a set of 2105 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 1 January 2023 coastal survey of Central California.
This is a set of 2076 oblique aerial photogrammetric images and their derivatives, collected from Point Lobos to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 12-13 September 2022 coastal survey of Central California.
This is a set of 3661 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 9 June 2022 coastal survey of Central California.
This is a set of 4595 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 12 March 2022 coastal survey of Central California.
This is a set of 2098 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 4 February 2022 coastal survey of Central California.
This is a set of 2269 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 20 January 2022 coastal survey of Central California.
This is a set of 2066 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 18 December 2021 coastal survey of Central California.
This is a set of 4722 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 September 2021 coastal survey of Central California.
This is a set of 2678 oblique aerial photogrammetric images and their derivatives, collected from PigeonPt to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 26 March 2021 coastal survey of Central California.
This is a set of 5626 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 3 March 2021 coastal survey of Central California.
This is a set of 2049 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 29 January 2021 coastal survey of Central California.
This is a set of 4919 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 11 January 2021 coastal survey of Central California.
This is a set of 3796 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 10 January 2021 coastal survey of Central California.
This is a set of 1896 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 15 October 2020 coastal survey of Central California.
This is a set of 1982 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 30 September 2020 coastal survey of Central California.
This is a set of 3862 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 July 2020 coastal survey of Central California.
This is a set of 1890 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 19 April 2020 coastal survey of Central California.
This is a set of 2889 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 19 March 2020 coastal survey of Central California.
This is a set of 4835 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 9 March 2020 coastal survey of Central California.
This is a set of 1979 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 25 January 2020 coastal survey of Central California.
This is a set of 1880 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 20 January 2020 coastal survey of Central California.
This is a set of 3072 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 30 November 2019 coastal survey of Central California.
This is a set of 1444 oblique aerial photogrammetric images and their derivatives, collected from Davenport to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 29 November 2019 coastal survey of Central California.
This is a set of 1782 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Davenport with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 31 October 2019 coastal survey of Central California.
This is a set of 1911 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 15 October 2019 coastal survey of Central California.
This is a set of 3777 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 10 June 2019 coastal survey of Central California.
This is a set of 5042 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 11 March 2019 coastal survey of Central California.
This is a set of 1967 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 4 March 2019 coastal survey of Central California.
This is a set of 2541 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 23 February 2019 coastal survey of Central California.
This is a set of 4734 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 10 September 2018 coastal survey of Central California.
This is a set of 5846 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 June 2018 coastal survey of Central California.
This is a set of 1533 oblique aerial photogrammetric images and their derivatives, collected from Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 28 May 2018 coastal survey of Central California.
This is a set of 3550 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 7 March 2018 coastal survey of Central California.
This is a set of 5355 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 29 January 2018 coastal survey of Central California.
This is a set of 5365 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 21 December 2017 coastal survey of Central California.
This is a set of 2072 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 18 December 2017 coastal survey of Central California.
This is a set of 2948 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 26 June 2017 coastal survey of Central California.
This is a set of 5069 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 13 June 2017 coastal survey of Central California.
This is a set of 757 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 31 May 2017 coastal survey of Central California.
This is a set of 410 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 27 May 2017 coastal survey of Central California.
This is a set of 642 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 19 May 2017 coastal survey of Central California.
This is a set of 3164 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 17 May 2017 coastal survey of Central California.
This is a set of 3045 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 12 May 2017 coastal survey of Central California.
This is a set of 628 oblique aerial photogrammetric images and their derivatives, collected from SeaCliff Beach to Fort Ord with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 May 2017 coastal survey of Central California.
This is a set of 1975 oblique aerial photogrammetric images and their derivatives, collected from Pedro Point to Sunset Beach with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 April 2017 coastal survey of Central California.
This is a set of 5044 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Cape San Martin with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 8 March 2017 coastal survey of Central California.
This is a set of 5642 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 22 February 2017 coastal survey of Central California.
This is a set of 4808 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Lucia with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 25 January 2017 coastal survey of Central California.
This is a set of 4521 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Cape San Martin with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 20 December 2016 coastal survey of Central California.
This is a set of 3036 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 1 December 2016 coastal survey of Central California.
This is a set of 3234 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 26 September 2016 coastal survey of Central California.
This is a set of 1569 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ano Nuevo with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 15 September 2016 coastal survey of Central California.
This is a set of 1600 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 March 2016 coastal survey of Central California.
This is a set of 2753 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 2 March 2016 coastal survey of Central California.
This is a set of 1309 oblique aerial photogrammetric images and their derivatives, collected from Santa Cruz to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 February 2016 coastal survey of Central California.
This is a set of 3494 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 26 January 2016 coastal survey of Central California.
This is a set of 1836 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 9 December 2015 coastal survey of Central California.
This is a set of 1132 oblique aerial photogrammetric images and their derivatives, collected from Capitola to Pajaro Dunes with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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USGS CoastCam at DUNEX: Timestack Imagery and Coordinate Data (Camera 2)
Two digital video cameras were temporarily installed at the U.S. Fish and Wildlife Service (USFWS) Pea Island National Wildlife Refuge (PINWR) in North Carolina (NC), as part of the DUring Nearshore Event eXperiment (DUNEX). DUNEX was a collaborative community-led experiment that took place in the fall of 2021 along the Outer Banks of NC, with the goal of improving the understanding, observational techniques, and predictive capabilities for extreme storm processes and impacts within the coastal environment. ... |
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USGS CoastCam at DUNEX: Timestack Imagery and Coordinate Data (Camera 1)
Two digital video cameras were temporarily installed at the U.S. Fish and Wildlife Service (USFWS) Pea Island National Wildlife Refuge (PINWR) in North Carolina (NC), as part of the DUring Nearshore Event eXperiment (DUNEX). DUNEX was a collaborative community-led experiment that took place in the fall of 2021 along the Outer Banks of NC, with the goal of improving the understanding, observational techniques, and predictive capabilities for extreme storm processes and impacts within the coastal environment. ... |
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Orthoimagery of Looe Key, Florida, 2022
A seabed orthoimage was developed from underwater images collected at Looe Key (LKR), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 850x160 meters (m) (0.13 square kilometers [km]) in size. It was created using image-averaging methods and saved as Geographic Tagged Image File Format ... |
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Orthoimagery of the coast of Alaska from Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents orthoimagery spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the orthoimages were created, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were collected with precise ... |
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Orthomosaic imagery for the intertidal zone at West Whidbey Island, WA, 2019-06-04
This portion of the data release presents a high-resolution orthomosaic image of the intertidal zone at West Whidbey Island, WA. The orthomosaic has a resolution of 2 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-04. The raw imagery used to create the orthomosaic was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed ... |
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Orthoimagery of Big Pine Ledge, Florida, 2021
A seabed orthoimage was developed from underwater images collected at Big Pine Ledge, Florida, in July 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 650x120 meters (0.078 square kilometers) in size. It was created using image-averaging methods and saved as a tiled GeoTIFF raster at 5-millimeter resolution. |
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RGB-averaged orthoimagery of coastal North Carolina, from 2020-02-08 to 2020-02-09
RGB-averaged orthoimages were created from aerial imagery collected February 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RGB-averaged orthoimages were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RGB-averaged orthoimages help researchers document inter-annual changes in shoreline position and coastal morphology in ... |
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Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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RGB-averaged orthoimagery of coastal North Carolina, on 2019-11-26, two-months Post-Hurricane Dorian
RGB-averaged orthoimages were created from aerial imagery collected on November 26, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions two-months after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface after Hurricane Dorian and were created to document ... |
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Aerial Imagery of the North Carolina Coast: 2020-05-08 to 2020-05-09
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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RGB-averaged orthoimagery of coastal North Carolina, on 2019-10-11, one-month post-Hurricane Dorian
RGB-averaged orthoimages were created from aerial imagery collected on October 11, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions one-month after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface after Hurricane Dorian and were created to document ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
RGB-averaged ortho products were created from aerial imagery collected between September 8 and 13, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface after Hurricane Dorian and were created to ... |
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Orthomosaic imagery from the UAS survey of the coral reef off Waiakane, Molokai, Hawaii, 24 June 2018
This portion of the data release presents a high-resolution orthomosaic image of the coral reef off Waiakane, Molokai, Hawaii. The orthomosaic has a resolution of 2.5 centimeters (cm) per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unoccupied aerial system (UAS) on 24 June 2018. The raw imagery used to create the orthomosaic was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2019-08-30 to 2019-09-02, Pre-Hurricane Dorian
Orthoimages were created from aerial imagery collected between August 30 and September 2, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions prior to Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface before Hurricane Dorian and were created to document ... |
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Orthomosaic imagery for the intertidal zone at Lone Tree Point, Kiket Bay, WA, 2019-06-05
This portion of the data release presents a high-resolution orthomosaic image of the intertidal zone at Lone Tree Point, Kiket Bay, WA. The orthomosaic has a resolution of 2 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-05. The raw imagery used to create the orthomosaic was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed ... |
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Aerial imagery from UAS survey of the intertidal zone at West Whidbey Island, WA, 2019-06-04
This portion of the data release presents the raw aerial imagery collected during the unmanned aerial system (UAS) survey of the intertidal zone at West Whidbey Island, WA, on 2019-06-04. The imagery was acquired using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. Flights using both a nadir camera orientation and an oblique camera orientation were conducted. For the nadir flights (F04, F05, F06, F07, and F08), the camera was mounted ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2020-05-08 to 2020-05-09
RGB-averaged orthoimages were created from aerial imagery collected May 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RGB-averaged orthoimages were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RGB-averaged orthoimages help researchers document inter-annual changes in shoreline position and coastal morphology in ... |
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Overlapping seabed images collected at Looe Key, Florida, 2021
A total of 94,567 underwater images were collected at Looe Key, Florida, in July 2021, using the SQUID5 Structure-from-Motion (SfM) system, a towed-surface vehicle with five downward-looking underwater cameras developed by the U.S. Geological Survey. The images are organized in zipped files grouped by survey line. The SQUID-5 records images in the Tagged Image File Format format to maintain the highest resolution and bit depth. Each image includes Exchangeable Image File (EXIF) metadata, containing Global ... |
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Low-altitude aerial imagery collected from a Helikite at Marconi Beach, Wellfleet, MA on March 22, 2023
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Orthoimagery of Looe Key, Florida, 2021
A seabed orthoimage was developed from underwater images collected at Looe Key, Florida, in July 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 720x100 meters (0.072 square kilometers) in size. It was created using image-mosaicking methods and saved as a tiled GeoTIFF raster at 5-millimeter resolution. |
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GNSS locations of seabed images collected at Looe Key, Florida, 2021
The text file "SQUID5_LKR_2021_Image_Locations.txt" provides the GNSS antenna location for underwater images collected at Looe Key, Florida, in July 2021, using the SQUID5 Structure-from-Motion (SfM) system, a towed-surface vehicle with five downward-looking underwater cameras developed by the U.S. Geological Survey. The GNSS antenna location for the time of each image capture is presented with greater precision than is stored in the individual image EXIF headers due to decimal place limitations of the EXIF ... |
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Orthomosaic representing Marconi Beach, Wellfleet from images acquired during field activity 2021-022-FA on March 17, 2021
The data in this publication map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide regional context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2021-022-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Orthomosaic representing Head of the Meadow Beach, Truro from images collected during field activity 2021-014-FA on February 4, 2021
These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2021-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In February 2021, U.S. Geological Survey and ... |
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Orthoimagery of Summerland Ledge, Florida, 2022
A seabed orthoimage was developed from underwater images collected at Summerland Ledge (SL), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 450x180 meters (m) (0.081 square kilometers [km]) in size. It was created using image-averaging methods and saved as a Geographic Tagged Image ... |
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Overlapping seabed images collected at Eastern Dry Rocks coral reef, Florida, 2021
Underwater images totaling 138,733 in number were collected at Eastern Dry Rocks coral reef, near Key West, Florida, in May 2021, using the SQUID5 Structure-from-Motion (SfM) system, a towed-surface vehicle with five downward-looking underwater cameras developed by the U.S. Geological Survey. The images are organized in zipped files grouped by survey line. The SQUID-5 records images as TIFF (.tif) format to maintain the highest resolution and bit depth. Each image includes EXIF metadata, containing GNSS ... |
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Overlapping lakebed images collected near Dollar Point, Lake Tahoe, CA, March 10 and 11, 2021
Underwater images were collected near Dollar Point, Lake Tahoe, CA, using a recently developed towed-surface vehicle with multiple downward-looking underwater cameras. The images are organized in zipped files grouped by survey line. The SQUID-5 system records images as TIFF (.tif) format to maintain the highest resolution and bit depth. Each image includes EXIF metadata, containing GNSS date, time, and latitude and longitude of the GNSS antenna mounted on the towed surface vehicle, copyright, keywords, and ... |
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GNSS locations of seabed images collected at Eastern Dry Rocks coral reef, Florida, 2021
This text file (SQUID5_EDR_2021_Image_Locations.txt) provides the GNSS antenna location for underwater images collected at Eastern Dry Rocks coral reef, near Key West, Florida, in May 2021, using the SQUID5 Structure-from-Motion (SfM) system, a towed-surface vehicle with five downward-looking underwater cameras developed by the U.S. Geological Survey. The GNSS antenna location for the time of each image capture is presented with greater precision than is stored in the individual image's EXIF header due to ... |
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GNSS locations of lakebed images collected near Dollar Point, Lake Tahoe, CA, March 10 and 11, 2021
This text file (2021-607-FA_Image_Locations.txt) provides the GNSS antenna location for underwater images collected near Dollar Point, Lake Tahoe, CA, using a recently developed towed-surface vehicle with multiple downward-looking underwater cameras. The GNSS antenna location for the time of each image capture is presented with greater precision than is stored in the individual image’s EXIF header due to decimal place limitations of the EXIF format. |
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Orthoimagery of Eastern Dry Rocks coral reef, Florida, 2021
A seabed orthoimage was developed from underwater images collected at Eastern Dry Rocks coral reef near Key West, Florida, in May 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 800x160 meters (0.12 square kilometers) in size, and it was created using image-mosaicking methods and saved as a tiled, 5-mm resolution raster. |
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Post-Hurricane Florence RGB averaged orthoimagery of coastal North Carolina
This data release presents structure-from-motion (SFM) products derived from aerial imagery surveys with precise Global Navigation Satellite System (GNSS) navigation data flown in a piloted fixed wing aircraft taken along the North Carolina coast in response to Hurricane Florence (available here https://coastal.er.usgs.gov/data-release/doi-P91KB9SF/). USGS researchers use the elevation models and orthorectified imagery to assess future coastal vulnerability, nesting habitats for wildlife, and provide data ... |
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Aerial Imagery of the North Carolina Coast: 2020-02-08 to 2020-02-09
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Shorelines Derived from Continuous Video-Imagery at the NASA-Kennedy Space Center, Florida From August 2011 to July 2012
In 2010, a video camera was installed near the northern boundary of the National Aeronautics and Space Administration-Kennedy Space Center (NASA-KSC) property along the Atlantic coast of Florida. A region extending 1 kilometer (km) to the south of the camera was established as the region of interest for the video image observations. During every daylight hour of camera operation from August 8, 2011 to July 24, 2012, a time exposure (timex) image product was created by averaging pixel color intensity for all ... |
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Orthoimagery of Lake Tahoe near Dollar Point
Lakebed orthoimagery was developed from underwater images collected near Dollar Point in Lake Tahoe, California, and processed using Structure-from-Motion (SfM) photogrammetry techniques. The orthoimages were developed using both image-mosaic and image-averaging methods, which were then output as 5-mm resolution rasters. In general, the "Mosaic" product is somewhat sharper in resolution but will include some distinct seam lines and noticeable differences in image quality across the image. The "Average" ... |
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Orthoimagery of Big Pine Ledge, Florida, 2022
A seabed orthoimage was developed from underwater images collected at Big Pine Ledge (BPL), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 800x160 meters (m) (0.12 square kilometers [km]) in size. It was created using image-averaging methods and saved as a tiled Geographic Tagged Image ... |
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USGS CoastCam at Sand Key, Florida: Timestack Imagery and Coordinate Data (Camera 2)
Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. Every hour during daylight hours, daily from 2018 to 2022, the cameras collected raw video and produced snapshots and time-averaged image products. For camera 2, one such product that is created is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup ... |
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USGS CoastCam at Madeira Beach, Florida: Timestack Imagery and Coordinate Data
A digital video camera was installed at Madeira Beach, Florida (FL) and faced west along the beach. Every hour during daylight hours, daily from 2017 to 2022, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the red, green, and blue or monochrome pixel ... |
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Orthomosaic images of the Whale's Tail Marsh region, South San Francisco Bay, CA
This portion of the data release presents orthomosaic images of the Whale's Tail Marsh region of South San Francisco Bay, CA. The orthomosaics have resolutions of 2.5 centimeters per pixel and were derived from structure-from-motion (SfM) processing of repeat aerial imagery collected from fixed-wing aircraft. The raw imagery used to create these elevation models was acquired from an approximate altitude of 427 meters (1,400 feet) above ground level (AGL), using a Hasselblad A6D-100c camera fitted with an HC ... |
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Aerial imagery from the UAS survey of the coral reef off Waiakane, Molokai, Hawaii, 24 June 2018
This portion of the data release presents raw aerial imagery collected during an unoccupied aerial system (UAS) survey of the coral reef off Waiakane, Molokai, Hawaii, on 24 June 2018. The imagery was acquired using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. The camera was mounted in a nadir orientation using a fixed mount. Before each flight, the camera’s digital ISO, aperture, and shutter speed were adjusted for ambient light ... |
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Aerial Imagery of the North Carolina Coast: 2019-11-26
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Low-altitude aerial imagery collected from a Helikite at Marconi Beach, Wellfleet, MA on March 22, 2024
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2024-016-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of two video cameras aimed at the beach (CoastCam CACO-02). In ... |
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Aerial Imagery of the North Carolina Coast: 2019-10-11
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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USGS CoastCam at Waiakāne, Moloka'i, Hawai'i: 2018 Timestack Imagery and Coordinate Data
A digital video camera was installed at Waiakāne, Moloka'i, Hawai'i (HI) and faced west along the beach. Every hour during daylight hours, daily from June 26, 2018, to September 20, 2018, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the red, green, and ... |
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USGS CoastCam at Isla Verde, Puerto Rico: 2018-2019 Timestack Imagery and Coordinate Data
A digital video camera was installed at Isla Verde Beach in San Juan, Puerto Rico (PR) and faced northeast along the beach. Every hour during daylight hours, daily from February 1, 2019, to July 15, 2019, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the ... |
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USGS CoastCam at Tres Palmas, Rincón, Puerto Rico: Timestack Imagery and Coordinate Data
A digital video camera was installed at Tres Palmas, Rincón, Puerto Rico (PR) and faced west along the beach. Every hour during daylight hours, daily from August 27, 2019, to March 10, 2020, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the red, green, ... |
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Imagery from USGS CoastCam deployed at Madeira Beach, Florida
A digital video camera was installed at Madeira Beach, Florida (FL) and faced west along the beach. Every hour during daylight hours, daily from 2017 to 2022, the camera collected raw video and produced snapshots and time-averaged image products. The images included in this data release were collected from January 21, 2017, to December 31, 2017. The camera is part of a U.S. Geological Survey (USGS) research project to study the beach and nearshore environment. USGS researchers analyzed the imagery collected ... |
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Orthomosaic imagery of the Los Padres Reservoir delta, Carmel River valley, CA, 2017-11-01
This portion of the data release presents a high-resolution orthomosaic image of the exposed Los Padres Reservoir delta where the Carmel River enters the reservoir. The orthomosaic has a resolution of 2.5 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unoccupied aerial system (UAS) on 2017-11-01. The raw imagery used to create the orthomosaic was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. ... |
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Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation
Coast Train is a library of images of coastal environments, annotations, and corresponding thematic label masks (or ‘label images’) collated for the purposes of training and evaluating machine learning (ML), deep learning, and other models for image segmentation. It includes image sets from both geospatial satellite, aerial, and UAV imagery and orthomosaics, as well as non-geospatial oblique and nadir imagery. Images include a diverse range of coastal environments from the U.S. Pacific, Gulf of Mexico, ... |
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Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Quicklook Orthoimage of Looe Key, Florida, 2022
A seabed orthoimage was developed from underwater images collected at Looe Key (LKR), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 850x160 meters (m) (0.13 square kilometers [km]) in size. This "quicklook" version of the dataset was created using image-averaging methods and saved as ... |
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Laboratory Observations of Oscillatory Flow Over Sand Ripples: Image Metadata
These data comprise laboratory observations of oscillatory flows over mobile sand ripples. The data were collected January 6-7, 2016, in the small-oscillatory flow tunnel (S-OFT) in the Sediment Dynamics Laboratory at the U.S. Naval Research Laboratory (NRL), Stennis Space Center, Mississippi (MS), while Donya Frank-Gilchrist was a National Research Council post-doctoral fellow there. The flow tunnel has a 2-m long acrylic test section which was filled with coarse quartz sand. A piston and flywheel were ... |
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Unprocessed aerial imagery from 31 August 2024 coastal survey of Washington.
This is a set of 6976 oblique aerial photogrammetric images and their derivatives, collected from Juan de Fuca Strait to Grays Harbor with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 6 July 2024 coastal survey of Washington.
This is a set of 7809 oblique aerial photogrammetric images and their derivatives, collected from Salish Sea with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 29 August 2022 coastal survey of Washington.
This is a set of 4281 oblique and near nadir aerial photogrammetric images and their derivatives, collected from Elwha river mouth to Ediz Hook CG with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the ... |
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Unprocessed aerial imagery from 28 August 2022 coastal survey of Washington.
This is a set of 4116 oblique aerial photogrammetric images and their derivatives, collected from Salish Sea with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 4 August 2020 coastal survey of Washington.
This is a set of 645 oblique aerial photogrammetric images and their derivatives, collected from Elwha river mouth to Ediz Hook CG with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 19 April 2023 thomas-fire survey of Southern California.
This is a set of 3086 vertical aerial photogrammetric images and their derivatives, collected from Montecito with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 23 January 2018 Thomas-fire survey of Southern California.
This is a set of 4838 oblique aerial photogrammetric images and their derivatives, collected from Montecito with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 18 March 2024 coastal survey of Southern California.
This is a set of 2076 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 23 February 2024 coastal survey of Southern California.
This is a set of 2371 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 12 February 2024 coastal survey of Southern California.
This is a set of 2032 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 5 January 2024 coastal survey of Southern California.
This is a set of 2061 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 12 October 2023 coastal survey of Southern California.
This is a set of 2013 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Port Hueneme with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 8 March 2023 coastal survey of Southern California.
This is a set of 2006 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 2 October 2022 coastal survey of Southern California.
This is a set of 1108 oblique aerial photogrammetric images and their derivatives, collected from Santa Rosa Island with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by ... |
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Unprocessed aerial imagery from 28 September 2022 coastal survey of Southern California.
This is a set of 2032 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 2 March 2022 coastal survey of Southern California.
This is a set of 2212 oblique aerial photogrammetric images and their derivatives, collected from Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 18 September 2020 coastal survey of Southern California.
This is a set of 1968 oblique aerial photogrammetric images and their derivatives, collected from Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 6 May 2020 coastal survey of Southern California.
This is a set of 2167 oblique aerial photogrammetric images and their derivatives, collected from Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 13 September 2018 coastal survey of Southern California.
This is a set of 2062 oblique aerial photogrammetric images and their derivatives, collected from Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 27 December 2017 coastal survey of Southern California.
This is a set of 2392 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Santa Barbara with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 1 March 2017 coastal survey of Southern California.
This is a set of 2979 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Ventura with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 28 September 2016 coastal survey of Southern California.
This is a set of 2671 oblique aerial photogrammetric images and their derivatives, collected from ptConception to Ventura with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Tile index for Alaska coastal orthoimagery and elevation data: Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents a shapefile that includes a spatial index of orthoimagery and elevation data describing the Alaskan coastline from Icy Cape to Cape Prince of Wales. The data products referenced in this index include orthoimagery, digital surface models, and elevation point clouds which were generated from aerial imagery using structure-from-motion methods. Fairbanks Fodar, a contracted mapping service, collected the aerial imagery in 2016 and created all of the data products ... |
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Unprocessed aerial imagery from 1 June 2023 coastal survey of Oregon and Washington.
This is a set of 10139 oblique aerial photogrammetric images and their derivatives, collected from Salish Sea WA to Seaside OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 29 August 2022 coastal survey of Oregon and Washington.
This is a set of 2413 oblique aerial photogrammetric images and their derivatives, collected from Taholah WA to Seaside OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 3 September 2020 coastal survey of Oregon and Washington.
This is a set of 2158 oblique aerial photogrammetric images and their derivatives, collected from NW WA to Seaside OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 3 August 2020 coastal survey of Oregon and Washington.
This is a set of 2324 oblique aerial photogrammetric images and their derivatives, collected from Taholah WA to Seaside OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 28 September 2017 coastal survey of Oregon and Washington.
This is a set of 2060 oblique aerial photogrammetric images and their derivatives, collected from OR-WA border to Nestucca River OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 25 September 2016 coastal survey of Oregon and Washington.
This is a set of 1712 oblique aerial photogrammetric images and their derivatives, collected from Cape Falcon to Cascade Head with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 19 April 2024 coastal survey of Northern California to Washington.
This is a set of 14032 oblique aerial photogrammetric images and their derivatives, collected from Hoh Head to Cape Mendocino with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 13 October 2018 coastal survey of Northern California to Washington.
This is a set of 11805 oblique aerial photogrammetric images and their derivatives, collected from OR-WA border to Mussel Rock CA with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 29 March 2018 coastal survey of Central and southern California.
This is a set of 1160 oblique aerial photogrammetric images and their derivatives, collected from Mud Creek Slide to Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera ... |
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Unprocessed aerial imagery from 23 February 2017 landslides survey of Central California.
This is a set of 5954 oblique aerial photogrammetric images and their derivatives, collected from San Francisco Bay area with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 26 January 2017 landslides survey of Central California.
This is a set of 4889 oblique aerial photogrammetric images and their derivatives, collected from San Francisco Bay area with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 4-5 November 2020 CZU-fire survey of Central California.
This is a set of 11776 near-nadir aerial photogrammetric images and their derivatives, collected from CZU fire with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 12 January 2023 coastal-landslides survey of Central California.
This is a set of 11207 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 6 January 2023 coastal-landslides survey of Central California.
This is a set of 8762 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 18 August 2024 coastal survey of Central California.
This is a set of 2003 oblique aerial photogrammetric images and their derivatives, collected from Point Lobos to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 17 June 2024 coastal survey of Central California.
This is a set of 5140 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 6 April 2024 coastal survey of Central California.
This is a set of 2286 oblique aerial photogrammetric images and their derivatives, collected from Point Lobos to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 7 March 2024 coastal survey of Central California.
This is a set of 2161 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 24 February 2024 coastal survey of Central California.
This is a set of 3059 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 23 February 2024 coastal survey of Central California.
This is a set of 2323 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 9 February 2024 coastal survey of Central California.
This is a set of 4787 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 12 January 2024 coastal survey of Central California.
This is a set of 1965 oblique aerial photogrammetric images and their derivatives, collected from Point Lobos to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 1 January 2024 coastal survey of Central California.
This is a set of 2876 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 29 December 2023 coastal survey of Central California.
This is a set of 1821 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 23 December 2023 coastal survey of Central California.
This is a set of 4772 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 26 October 2023 coastal survey of Central California.
This is a set of 2869 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 11 October 2023 coastal survey of Central California.
This is a set of 4930 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 10 October 2023 coastal survey of Central California.
This is a set of 3929 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 June 2023 coastal survey of Central California.
This is a set of 2123 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 6 April 2023 coastal survey of Central California.
This is a set of 2374 vertical aerial photogrammetric images and their derivatives, collected from Half Moon Bay to Santa Cruz with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 17 March 2023 coastal survey of Central California.
This is a set of 2077 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 16 March 2023 coastal survey of Central California.
This is a set of 2915 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 13 March 2023 coastal survey of Central California.
This is a set of 2195 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 3 March 2023 coastal survey of Central California.
This is a set of 2758 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Remote Sensing Coastal Change Simple Data Distribution Service
The Remote Sensing Coastal Change Simple Data Service provides timely and long-term access to emergency, provisional, and approved photogrammetric imagery, derivatives, and ancillary data through a web service via HyperText Transfer Protocol to a folder/file structure organized by data collection platform and survey (collection effort) with metadata sufficient to facilitate both human and machine access. Data are acquired, processed, and published using standardized workflows. Each data type added to the ... |
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Digital surface models of Pea Island National Wildlife Refuge DUNEX Site, North Carolina in September and October 2021
The data in this part of the release are digital surface models (DSMs) that characterize the beach at the USGS DUring Nearshore Event eXperiment (DUNEX) site on Pea Island National Wildlife Refuge, NC. DUNEX is a multi-agency, academic, and non-governmental organization collaborative community experiment designed to study nearshore coastal processes during storm events. USGS participation in DUNEX will contribute new measurements and models that will increase our understanding of storm impacts to coastal ... |
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Time Series of Structure-from-Motion Products - RGB Orthomosaics: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of red ... |
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Time Series of Structure-from-Motion Products - Multispectral Orthomosaics: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Time Series of Structure-from-Motion Products - Digital Elevation Models: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of DEMs ... |
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Refraction-corrected bathymetric point cloud from the UAS survey of the coral reef off Waiakane, Molokai, Hawaii, 24 June 2018
This portion of the data release presents a bathymetric point cloud from an unoccupied aerial system (UAS) survey of the coral reef off Waiakane, Molokai, Hawaii, on 24 June 2018. The point cloud has been corrected for the effects of refraction using the techniques described in Dietrich (2017a). The point cloud was created from structure-from-motion (SfM) processing of aerial imagery collected using a UAS with a Ricoh GR II digital camera fitted with a circular polarizing filter. During the survey, a ... |
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Ground control point locations for the UAS survey of the coral reef off Waiakane, Molokai, Hawaii, 24 June 2018
This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during an unoccupied aerial system (UAS) survey of the coral reef off Waiakane, Molokai, Hawaii, on 24 June 2018. Twenty temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of: nine submerged targets consisting of small (80 centimeter X 80 centimeter) ... |
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Topographic point cloud for the intertidal zone at Lone Tree Point, Kiket Bay, WA, 2019-06-05
This portion of the data release presents a topographic point cloud of the intertidal zone at Lone Tree Point, Kiket Bay, WA. The point cloud was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-05. The point cloud has 206,323,353 points with an average point density of 929 points per-square meter. The point cloud is tiled to reduce individual file sizes and is grouped within a zip file for downloading. Each point in the point ... |
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Ground control point locations for UAS survey of the intertidal zone at Lone Tree Point, Kiket Bay, WA, 2019-06-05
This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during an unmanned aerial system (UAS) survey of the intertidal zone at Lone Tree Point, Kiket Bay, WA on 2019-06-05. Eighteen temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns ... |
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Topographic point cloud for the intertidal zone at West Whidbey Island, WA, 2019-06-04
This portion of the data release presents a topographic point cloud of the intertidal zone at West Whidbey Island, WA. The point cloud was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-04. The point cloud has 293,261,002 points with an average point density of 1,063 points per-square meter. The point cloud is tiled to reduce individual file sizes and is grouped within a zip file for downloading. Each point in the point cloud ... |
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Ground control point locations for UAS survey of the intertidal zone at West Whidbey Island, WA, 2019-06-04
This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during an unmanned aerial system (UAS) survey of the intertidal zone at West Whidbey Island, WA on 2019-06-04. Twenty-five temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and ... |
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Digital surface model (DSM) for the intertidal zone at West Whidbey Island, WA, 2019-06-04
This portion of the data release presents a digital surface model (DSM) and hillshade image of the intertidal zone at West Whidbey Island, WA. The DSM has a resolution of 4 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-04. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been ... |
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Time Series of Structure-from-Motion Products - Point Clouds: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Orthomosaics representing Nauset Light Beach, Eastham, MA on September 14 and 20, 2023, pre and post Hurricane Lee
The data in this release map Marconi Beach, Head of the Meadow Beach, and Nauset Light Beach, in Cape Cod National Seashore (CACO), Massachusetts, before and after Hurricane Lee in September 2023. U.S Geological Survey personnel conducted field surveys to collect topographic data using global navigation satellite systems (GNSS) at all three beaches. In addition, at Nauset Light Beach, an uncrewed aerial system (UAS) was used to collect images with a Ricoh GRII camera for use in structure from motion ... |
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Digital surface models representing Nauset Light Beach, Eastham, MA on September 14 and 20, 2023, pre and post Hurricane Lee
The data in this release map Marconi Beach, Head of the Meadow Beach, and Nauset Light Beach, in Cape Cod National Seashore (CACO), Massachusetts, before and after Hurricane Lee in September 2023. U.S Geological Survey personnel conducted field surveys to collect topographic data using global navigation satellite systems (GNSS) at all three beaches. In addition, at Nauset Light Beach, an uncrewed aerial system (UAS) was used to collect images with a Ricoh GRII camera for use in structure from motion ... |
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Point cloud data of Looe Key, Florida, 2022
A three-dimensional point cloud (LAZ format) was developed from underwater images collected at Looe Key (LKR), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, and RGB colors derived from the color-corrected imagery. LIDAR Aerial Survey files (LAS) - and its compressed form, LAZ - is an open format developed for ... |
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Orthomosaic of Looe Key, Florida, 2022
A seabed orthoimage was developed from underwater images collected at Looe Key (LKR), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 850x160 meters (m) (0.13 square kilometers [km]) in size. It was created using image-mosaicking methods and saved as Geographic Tagged Image File Format ... |
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Quicklook Digital Elevation Model (DEM) of Looe Key, Florida, 2022
A digital elevation model (DEM) was created from underwater images collected at Looe Key (LKR), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques into a classified two-class ('unclassified' and 'low noise') 3D point cloud. The DEM was created in Metashape (ver. 1.6.6) from the point cloud and includes points from both classes. The DEM covers a rectangular ... |
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High Resolution Digital Elevation Model (DEM) of Looe Key, Florida, 2022
A digital elevation model (DEM) was created from underwater images collected at Looe Key (LKR), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques into a classified two-class ('unclassified' and 'low noise') 3D point cloud. The DEM was created in Metashape (ver. 1.6.6) from the point cloud and includes points from both classes. The DEM covers a rectangular ... |
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Point cloud data of Summerland Ledge, Florida, 2022
A three-dimensional point cloud (LAZ format) was developed from underwater images collected at Summerland Ledge (SL), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, and RGB colors derived from the color-corrected imagery. LIDAR Aerial Survey files (LAS) - and its compressed form, LAZ - is an open format ... |
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Orthomosaic of Summerland Ledge, Florida, 2022
A seabed orthoimage was developed from underwater images collected at Summerland Ledge (SL), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 450x180 meters (m) (0.081 square kilometers [km]) in size. It was created using image-mosaicing methods and saved as a Geographic Tagged Image ... |
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Digital Elevation Model (DEM) of Summerland Ledge, Florida, 2022
A digital elevation model (DEM) was created from underwater images collected at Summerland Ledge (SL), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques into a classified two-class ('unclassified' and 'low noise') 3D point cloud. The DEM was created in Metashape (ver. 1.6.6) from the point cloud and includes points from both classes. The DEM covers a ... |
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Point cloud data of Big Pine Ledge, Florida, 2022
A three-dimensional point cloud (LAZ format) was developed from underwater images collected at Big Pine Ledge (BPL), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, and RGB colors derived from the color-corrected imagery. LIDAR Aerial Survey files (LAS) – and its compressed form, LAZ – is an open format ... |
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Orthomosaic of Big Pine Ledge, Florida, 2022
A seabed orthoimage was developed from underwater images collected at Big Pine Ledge (BPL), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 800x160 meters (m) (0.12 square kilometers [km]) in size. It was created using image-mosaicking methods and saved as a tiled Geographic Tagged ... |
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Digital elevation model (DEM) of Big Pine Ledge, Florida, 2022
A digital elevation model (DEM) was created from underwater images collected at Big Pine Ledge (BPL), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system. The underwater images were processed using SfM photogrammetry techniques into a classified two-class ('unclassified' and 'low noise') 3D point cloud. The DEM was created in Metashape (ver. 1.8.5) from the point cloud and includes points from both classes. The DEM covers a ... |
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Structure from motion GCPs, digital surface model, and orthomosaic representing Marconi Beach, Wellfleet, MA on March 22, 2024
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2024-016-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of two video cameras aimed at the beach (CoastCam CACO-02). In ... |
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Town Neck Beach, Massachusetts, 5 cm 2016-2017 Orthomosaics
Low-altitude (80-100 meters above ground level) Unmanned Aircraft Systems (UAS) imagery of Town Neck Beach in Sandwich, Massachusetts, were used in a structure-from-motion (SfM) photogrammetry workflow to create high-resolution topographic datasets. Imagery was collected at close to low tide on twelve days to observe changes in beach and dune morphology. Ground control points (GCPs), which are temporary targets on the ground located by using a real-time kinematic global navigation satellite system (RTK-GNSS ... |
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Town Neck Beach, Massachusetts, 10 cm 2016-2017 Digital Elevation Models
Low-altitude (80-100 meters above ground level) Unmanned Aircraft Systems (UAS) imagery of Town Neck Beach in Sandwich, Massachusetts, were used in a structure-from-motion (SfM) photogrammetry workflow to create high-resolution topographic datasets. Imagery was collected at close to low tide on twelve days to observe changes in beach and dune morphology. Ground control points (GCPs), which are temporary targets on the ground located by using a real-time kinematic global navigation satellite system (RTK-GNSS ... |
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SfM digital surface model and orthomosaic representing Head of the Meadow Beach, Truro, MA on March 20, 2024
The data in this release re-map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA and provide updated environmental context for the 2020 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-01, which are two video cameras aimed at the beach. In February and March 2024, U.S. ... |
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Point cloud data of Lake Tahoe near Dollar Point
Three-dimensional point clouds (LAZ format) were developed from underwater images collected near Dollar Point in Lake Tahoe, California, and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, RGB colors, Metashape-computed confidence values, and a two-class classification ('unclassified' and 'high noise') derived from the confidence values. LAZ is an open format developed for the efficient use of point cloud lidar data. A description of the LAZ ... |
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Point cloud data of Big Pine Ledge, Florida, 2021
A three-dimensional point cloud (LAZ format) was developed from underwater images collected at Big Pine Ledge, Florida, in July 2021 using the SQUID-5 camera system and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, and RGB colors derived from the color-corrected imagery. LAS (and its compressed form, LAZ) is an open format developed for the efficient use of point cloud lidar data. |
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Digital elevation model (DEM) of Big Pine Ledge, Florida, 2021
A digital elevation model (DEM) was created from underwater images collected at Big Pine Ledge, Florida, in July 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques into a classified two-class ('unclassified' and 'low noise') 3D point cloud. The DEM was created in Metashape (ver. 1.6.6) from the point cloud, and includes points from both classes. The DEM covers a rectangular area of seafloor approximately 650x120 meters (0 ... |
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Refraction-corrected bathymetric digital surface model (DSM) from the UAS survey of the coral reef off Waiakane, Molokai, Hawaii, 24 June 2018
This portion of the data release presents a bathymetric digital surface model (DSM) from an unoccupied aerial system (UAS) survey of the coral reef off Waiakane, Molokai, Hawaii, on 24 June 2018. The DSM has a horizontal resolution of 10 centimeters per pixel and has been corrected for the effects of refraction using the techniques described in Dietrich (2017a). The DSM was created from structure-from-motion (SfM) processing of aerial imagery collected using a UAS with a Ricoh GR II digital camera fitted ... |
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Digital surface model (DSM) for the intertidal zone at Lone Tree Point, Kiket Bay, WA, 2019-06-05
This portion of the data release presents a digital surface model (DSM) and hillshade image of the intertidal zone at Lone Tree Point, Kiket Bay, WA. The DSM has a resolution of 4 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-05. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have ... |
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Orthomosaic representing Head of the Meadow Beach, Truro, MA on March 10, 2023
The data in this release re-map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA and provide updated environmental context for the 2020 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-01, which are two video cameras aimed ... |
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Digital Surface Model representing Head of the Meadow Beach, Truro, MA on March 10, 2023
The data in this release re-map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA and provide updated environmental context for the 2020 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-01, which are two video cameras aimed ... |
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Orthomosaic representing Marconi Beach, Wellfleet, MA on March 22, 2023
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Digital Surface Model representing Marconi Beach, Wellfleet, MA on March 22, 2023
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Bathymetric digital elevation model (DEM) of Lake Tahoe near Dollar Point
Underwater images collected near Dollar Point in Lake Tahoe, California, were processed using Structure-from-Motion (SfM) photogrammetry techniques into a classified 3D point cloud. The DEM was derived in Metashape (ver. 1.6.4) from the point cloud, but it excludes the 'high noise' class. The DEM data were output as a geoTIFF raster at 25-mm resolution. |
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Digital Surface Models (DSMs) of the Whale's Tail Marsh region, South San Francisco Bay, CA
This portion of the data release presents digital surface models (DSM) of the Whale's Tail Marsh region of South San Francisco Bay, CA. The DSMs have resolutions of 5 centimeters per pixel and were derived from structure-from-motion (SfM) processing of repeat aerial imagery collected from fixed-wing aircraft. Unlike a digital elevation model (DEM), a DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, structures, and other objects have not been removed from the data. ... |
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Digital elevation models (DEMs) of coastal North Carolina, on 2019-10-11, one month Post-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected October 11, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions one-month after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface one-month post-Hurricane Dorian and were created to document inter-annual changes in ... |
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High resolution structure from motion digital surface models representing three sites on North Core Banks, NC in October 2022
These data map in high detail surficial cross-sections of North Core Banks, a barrier island in Cape Lookout National Seashore, NC, in October 2022. U.S. Geological Survey field efforts are part of an interagency agreement with the National Park Service to monitor the recovery of the island from Hurricanes Florence (2018) and Dorian (2019). Three sites of outwash, overwash, and pond formation were targeted for extensive vegetation ground-truthing, sediment samples, bathymetric mapping with a remote ... |
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Topographic point cloud of the Los Padres Reservoir delta, Carmel River valley, CA, 2017-11-01
This portion of the data release presents a topographic point cloud of the exposed Los Padres Reservoir delta where the Carmel River enters the reservoir. The point cloud was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unoccupied aerial system (UAS) on 2017-11-01. The point cloud has 115,819,907 points with an average point density of 611 points per-square meter. Each point in the point cloud contains an explicit horizontal and vertical coordinate, color, ... |
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Ground control point locations for the UAS survey of the Los Padres Reservoir delta, Carmel River valley, CA, 2017-11-01
This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during the unoccupied aerial system (UAS) survey of the Los Padres Reservoir delta, Carmel River valley, CA, 2017-11-01. Twenty temporary ground control points (GCPs) consisting of small square tarps with black-and-white cross patterns were distributed throughout the area to establish survey control. The GCP positions were measured ... |
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Digital surface model (DSM) and digital elevation model (DEM) of the Los Padres Reservoir delta, Carmel River valley, CA, 2017-11-01
This portion of the data release presents a digital surface model (DSM) and digital elevation model (DEM) of the exposed Los Padres Reservoir delta where the Carmel River enters the reservoir. The DSM and DEM have a resolution of 10 centimeters per pixel and were derived from structure-from-motion (SfM) processing of aerial imagery collected with an unoccupied aerial system (UAS) on 2017-11-01. The DSM represents the elevation of the highest object within the bounds of a cell, including vegetation, woody ... |
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Elevation point clouds of the coast of Alaska from Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents georeferenced elevation point clouds spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the point clouds were derived, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were ... |
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Digital elevation models of the coast of Alaska from Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents digital elevation models (DEMs) spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the DEMs were created, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were collected ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2020-05-08 to 2020-05-09
Digital elevation models (DEMs) were created from aerial imagery collected May 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers document inter-annual changes in shoreline position and coastal morphology in response to storm events using aerial ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2020-02-08 to 2020-02-09
Digital elevation models (DEMs) were created from aerial imagery collected February 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers document inter-annual changes in shoreline position and coastal morphology in response to storm events using ... |
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Digital elevation models (DEMs) of coastal North Carolina, on 2019-11-26, two-months Post-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected November 26, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions two-months after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface one-month post-Hurricane Dorian and were created to document inter-annual changes in ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected between September 08 and September 13, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions post-Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface after Hurricane Dorian and were created to document inter-annual changes in ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2019-08-30 to 2019-09-02, Pre-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected between August 30 and September 2, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions prior to Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface before Hurricane Dorian and were created to document inter-annual changes in ... |
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Orthomosaic representing Marconi Beach, Wellfleet, MA March 11, 2022
The data in this release map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Digital surface model representing Marconi Beach, Wellfleet on March 11, 2022
The data in this release map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Point cloud data of Looe Key, Florida, 2021
A three-dimensional point cloud (LAZ format) was developed from underwater images collected at Looe Key, Florida, in July 2021 using the SQUID-5 camera system and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, and RGB colors derived from the color-corrected imagery. LAS (and its compressed form, LAZ) is an open format developed for the efficient use of point cloud lidar data. |
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Digital elevation model (DEM) of Looe Key, Florida, 2021
A digital elevation model (DEM) was created from underwater images collected at Looe Key, Florida, in July 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques into a classified two-class ('unclassified' and 'low noise') 3D point cloud. The DEM was created in Metashape (ver. 1.6.6) from the point cloud, and includes points from both classes. The DEM covers a rectangular area of seafloor approximately 720x100 meters (0.072 ... |
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Orthomosaic representing Head of the Meadow Beach, Truro on March 10, 2022
These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In March 2022, U.S. Geological Survey and Woods ... |
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Digital surface model representing Head of the Meadow Beach, Truro on March 10, 2022
These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In March 2022, U.S. Geological Survey and Woods ... |
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Point cloud data of Eastern Dry Rocks coral reef, Florida, 2021
A three-dimensional point cloud (LAZ format) was developed from underwater images collected at Eastern Dry Rocks coral reef near Key West, Florida, in May 2021 using the SQUID-5 camera system and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, RGB colors, Metashape-computed confidence values, and a two-class classification ('unclassified' and 'low noise') derived from the confidence values. LAS (and its compressed form, LAZ) is an open format ... |
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Bathymetric digital elevation model (DEM) of Eastern Dry Rocks coral reef, Florida, 2021
A digital elevation model (DEM) was created from underwater images collected at Eastern Dry Rocks coral reef near Key West, Florida, in May 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques into a classified two-class ('unclassified' and 'low noise') 3D point cloud. The DEM was derived in Metashape (ver. 1.6.5) from the point cloud, but it excludes the 'low noise' class. The DEM covers a rectangular area of seafloor ... |
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Post-Hurricane Florence Digital Elevation Models of coastal North Carolina
This data release presents structure-from-motion (SFM) products derived from aerial imagery surveys with precise Global Navigation Satellite System (GNSS) navigation data flown in a piloted fixed wing aircraft taken along the North Carolina coast in response to Hurricane Florence (available here https://coastal.er.usgs.gov/data-release/doi-P91KB9SF/). USGS researchers use the elevation models and orthorectified imagery to assess future coastal vulnerability, nesting habitats for wildlife, and provide data ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2018-01-29
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-12-21
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-12-07
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry with ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-10-12
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-06-13
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-27
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-19
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point Cloud Coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-03-08
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 1967-10-18
Presented here is a point cloud produced by the U.S. Geological Survey (USGS) from historical U.S. Air Force vertical aerial imagery, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was downloaded from USGS Eros Data Center and processed using structure-from-motion ... |
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Laboratory Observations of Oscillatory Flow Over Sand Ripples: Velocity Metadata
These data comprise laboratory observations of oscillatory flows over mobile sand ripples. The data were collected January 6-7, 2016, in the small-oscillatory flow tunnel (S-OFT) in the Sediment Dynamics Laboratory at the U.S. Naval Research Laboratory (NRL), Stennis Space Center, Mississippi (MS), while Donya Frank-Gilchrist was a National Research Council post-doctoral fellow there. The flow tunnel has a 2-m long acrylic test section which was filled with coarse quartz sand. A piston and flywheel were ... |
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PCMSC PlaneCam – Field data from periodic and event-response surveys of the U.S. West Coast.
This is an ongoing collection of aerial oblique and near-nadir images, ancillary data, and derivatives, from aerial surveys of coastal and near-coastal environments with a crewed light aircraft using the "PCMSC PlaneCam," a mounted fixed-lens DSLR camera with an attached consumer-grade GPS for time-keeping and approximate position, and a Global Navigation Satellite System (GNSS) for precise positioning. Data are collected and produced primarily for coastal monitoring using structure-from-motion ... |
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USGS CoastCam at DUNEX: Intrinsic and Extrinsic Calibration Data (Camera 2)
Two digital video cameras were temporarily installed at the U.S. Fish and Wildlife Service (USFWS) Pea Island National Wildlife Refuge (PINWR) in North Carolina (NC), as part of the DUring Nearshore Event eXperiment (DUNEX). DUNEX was a collaborative community-led experiment that took place in the fall of 2021 along the Outer Banks of NC, with the goal of improving the understanding, observational techniques, and predictive capabilities for extreme storm processes and impacts within the coastal environment. ... |
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USGS CoastCam at DUNEX: Intrinsic and Extrinsic Calibration Data (Camera 1)
Two digital video cameras were temporarily installed at the U.S. Fish and Wildlife Service (USFWS) Pea Island National Wildlife Refuge (PINWR) in North Carolina (NC), as part of the DUring Nearshore Event eXperiment (DUNEX). DUNEX was a collaborative community-led experiment that took place in the fall of 2021 along the Outer Banks of NC, with the goal of improving the understanding, observational techniques, and predictive capabilities for extreme storm processes and impacts within the coastal environment. ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Smith Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Ship Shoal Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Rhode Island National Wildlife Refuge, RI, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parker River, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parramore Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Myrtle Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Monomoy Island, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Fisherman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cobb Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Coast Guard Beach, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-06-26
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Lookout, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Hatteras, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Assawoman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Assateague Island, MD & VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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2D micromodel studies of pore-throat clogging by pure fine-grained sediments and natural sediments from NGHP-02, offshore India
Fine-grained sediments, or “fines,” are nearly ubiquitous in natural sediments, even in the predominantly coarse-grained sediments that host gas hydrates. Fines within these sandy sediments can be mobilized and subsequently clog flow pathways while methane is being extracted from gas hydrate as an energy resource. Using two-dimensional (2D) micromodels to test the conditions in which clogging occurs provides insights for choosing production operation parameters that optimize methane recovery in the ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2014–2015
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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USGS CoastCam at Sand Key, Florida: Intrinsic and Extrinsic Calibration Data (Camera 2)
Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. Every hour during daylight hours, the cameras collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The images included in this data release were collected by camera 2 (c2) from May 29, ... |
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USGS CoastCam at Sand Key, Florida: Intrinsic and Extrinsic Calibration Data (Camera 1)
Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. Every hour during daylight hours, the cameras collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements.. The cameras are part of a U.S. Geological Survey (USGS) research project to study the ... |
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USGS CoastCam at Waiakāne, Moloka'i, Hawai'i: 2018 Intrinsic and Extrinsic Calibration Data
A digital video camera was installed at Waiakāne, Moloka'i, Hawai'i (HI) and faced west along the beach. Every hour during daylight hours, daily from June 26, 2018, to September 20, 2018, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological Survey (USGS) research ... |
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USGS CoastCam at Tres Palmas, Rincón, Puerto Rico: Intrinsic and Extrinsic Calibration Data
A digital video camera was installed at Tres Palmas, Rincón, Puerto Rico (PR) and faced west to view the beach and water offshore. Every hour during daylight hours, daily from August 27, 2019 to March 10, 2020, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological ... |
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USGS CoastCam at Isla Verde, Puerto Rico: 2018-2019 Intrinsic and Extrinsic Calibration Data
A digital video camera was installed at Isla Verde, Puerto Rico (PR) and faced northeast along the beach. Every hour during daylight hours, daily from February 1, 2019, to July 15, 2019, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological Survey (USGS) research ... |
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Intrinsic and Extrinsic Calibration Data From USGS CoastCam deployed at Madeira Beach, Florida
A digital video camera was installed at Madeira Beach, Florida (FL) and faced west along the beach. Every hour during daylight hours, daily from 2017 to 2022, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological Survey (USGS) research project to study the beach and ... |
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Orthomosaics of Pea Island National Wildlife Refuge DUNEX Site, North Carolina in September and October 2021
The data in this part of the release are orthomosaics that characterize the beach at the USGS DUring Nearshore Event eXperiment (DUNEX) site on Pea Island National Wildlife Refuge, NC. During September and October 2021, USGS and Woods Hole Oceanographic Institute (WHOI) scientists conducted multiple field surveys to collect a topobathy elevation time series. Images of the beach for use in structure from motion were taken with a camera attached to a helium filled balloon-kite (Helikite). Agisoft Metashape (v ... |
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