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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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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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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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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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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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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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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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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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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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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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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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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In situ seafloor images from the lower Columbia River, Washington and Oregon, 2021
In situ seafloor images were acquired at four sites (SKM, SLG, LDB, WLW) in the lower Columbia River, Washington and Oregon, with an underwater camera system between June 5 and June 8, 2021. Between 248 and 427 digital images of the sediment surface were collected at each site with an underwater camera system that was repeatedly lowered to the seabed along a series of 1 km-long transects oriented along the main navigation channel and spaced about 60 m apart. The camera consisted of a FLIR Blackfly BFS-PGE ... |
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Surface sediment grain diameter measurements from point counts of in situ seafloor images collected in the lower Columbia River, Washington and Oregon, 2021
This dataset contains surface sediment grain diameter measurements from in situ seafloor images collected in the lower Columbia River, Washington and Oregon, in 2021. Surface sediment grain diameters were derived from manual measurements (or "point counts") in a subset of images used to calibrate and validate an automated image processing algorithm to determine surface sediment grain size distributions. For each calibration and validation image that was selected, the long and short axis of 100 grains were ... |
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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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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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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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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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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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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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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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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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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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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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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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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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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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Surface sediment grain size distributions derived from manual point counts of in situ seafloor images from the lower Columbia River, Washington and Oregon, 2021
This dataset contains surface sediment grain size distributions derived from manual point counts of in situ seafloor images obtained with an underwater camera system in the lower Columbia River, Washington and Oregon, in 2021. The distributions derived from manual point counts were compared with results from an automated image processing technique to calibrate and validate the automated method used to quantify surface sediment grain size distributions in objective images. The surface sediment grain size ... |
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Vessel-mounted acoustic Doppler current profiler (ADCP) data from the lower Columbia River, Washington and Oregon, 2021
This dataset contains water velocity data derived from spatial surveys performed with a vessel-mounted acoustic Doppler current profiler at four sites (SKM, SLG, LDB, WLW) in the lower Columbia River, Washington and Oregon, in 2021. The data are provided in netCDF (.nc) format and compressed into .zip archives for each site. |
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Near-bed velocity measurements in Monterey Bay during arrival of the 2010 Chile Tsunami
On February 27, 2010, a tsunami originating near Chile arrived in Monterey Bay, California. This data release comprises two hours of pressure and near-bed velocity data spanning the largest tsunami waves. At the time, the U.S. Geological Survey Pacific Coastal and Marine Science Center had a remotely-controlled instrumented platform deployed adjacent to the Santa Cruz Municipal Wharf (mean depth 9 m) for collecting hydrodynamic and sediment transport data. In anticipation of the arrival of the tsunami, ... |
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Single-beam bathymetry data collected in 2009 in the vicinity of Wainwright, Alaska
Bathymetry data were collected in the Wainwright Inlet, the mouth of the Kuk River, and in the nearshore region off Wainwright, Alaska, in August of 2009. Bathymetry was measured with a single-beam echo-sounder (10 Hz 144 ODOM Echotrac CV-100) mounted on the stern of a small vessel and synchronized to a 145 Real-Time Kinematic (RTK) Global Positioning Systems (GPS). The depth is measured relative to approximate Mean Sea Level (see attribute accuracy report in this file for further details on the MSL), and ... |
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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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Bathymetry and seafloor acoustic backscatter of mobile subaqueous sand dunes in the lower Columbia River, Washington and Oregon, 2021
Bathymetry and seafloor acoustic backscatter data were collected at four sites (SKM, SLG, LDB, WLW) using a SWATHPlus interferometric sonar (234 kHz) pole mounted to the R/V Parke Snavely during a June 2021 survey of the lower Columbia River, Washington and Oregon. Each site was surveyed repeatedly between June 5 and June 9, 2021 to quantify bathymetric changes resulting from migration of subaqueous sand dunes. The bathymetry and seafloor acoustic backscatter data from each site are provided as GeoTIFF ... |
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Surface sediment grain size distributions derived from automated image processing of in situ seafloor images from the lower Columbia River, Washington and Oregon, 2021
This dataset contains surface sediment grain size distributions derived from automated image processing of in situ seafloor images obtained with an underwater camera system at four sites (SKM, SLG, LDB, WLW) in the lower Columbia River, Washington and Oregon, in 2021. The surface sediment grain size distribution data are provided in comma-separated text (.csv) format for each site and for data used in calibration and validation of the automated image processing technique. |
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Backscatter-Oregon OCS Floating Wind Farm Site
This Data Release contains data from the U.S. Geological Survey (USGS) survey of the Oregon outer Continental shelf (OCS) Floating Wind Farm Site in 2014. The backscatter intensity data was collected along with bathymetry data by USGS during the period from August 20 to September 1, 2014, using a Reson 7111 multibeam echosounder. The mapping mission collected bathymetry data from about 163 m to 566 m depths on the Oregon outer continental shelf. The acquisition was funded by the U.S. Bureau of Ocean Energy ... |
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Bathymetry Hillshade-Oregon OCS Floating Wind Farm Site
This Data Release contains data from the USGS survey of the Oregon OCS Floating Wind Farm Site in 2014. The shaded-relief raster was generated from bathymetry data collected by USGS during the period from August 20 to September 1, 2014. using a Reson 7111 multibeam echosounder. The mapping mission collected bathymetry data from about 163 m to 566 m depths on the Oregon outer continental shelf. The acquisition was funded by the U.S. Bureau of Ocean Energy Management. |
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Bathymetry-Oregon OCS Floating Wind Farm Site
This Data Release contains data from the U.S. Geological Survey (USGS) survey of the Oregon outer continental shelf (OCS) Floating Wind Farm Site in 2014. The bathymetry raster was generated from bathymetry data collected by USGS during the period from August 20 to September 1, 2014, using a Reson 7111 multibeam echosounder. The mapping mission collected bathymetry data from about 163 m to 566 m depths on the Oregon outer continental shelf. The acquisition was funded by the U.S. Bureau of Ocean Energy ... |
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Contours-Oregon OCS Floating Wind Farm Site
This data release contains data from the USGS field activity 2014-607-FA, a survey of the Oregon Outer Continental Shelf (OCS) Floating Wind Farm Site in 2014. The bathymetry raster was generated from bathymetry data collected by U.S. Geological Survey (USGS) during the period from August 20 to September 1, 2014 using a Reson 7111 multibeam echosounder. The mapping mission collected bathymetry data from about 163 m to 566 m depths on the Oregon outer continental shelf. The acquisition was funded by the U.S. ... |
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Geologic Observations-Oregon OCS Floating Wind Farm Site
This part of the Oregon Outer Continental Shelf (OCS) Floating Windfarm Suite Data Release presents geological observations from video collected on U.S. Geological Survey (USGS) field activity 2014-607-FA in the Floating Wind Farm survey area. The survey was conducted using 12 hour day operations out of Charleston Harbor near Coos Bay, Oregon. The cruise plan consisted of 23 days on site split between sonar mapping and video ground truth surveying. Activities parsed out to nine days of sonar mapping, three ... |
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Mammal Observations-Oregon OCS Floating Wind Farm Site
This part of the Oregon OCS Data Release presents marine mammal observations from U.S. Geological Survey (USGS) field activity 2014-607-FA in the Oregon Outer continental Shelf (OCS)Floating Wind Farm survey area. The survey was conducted using 12 hour day operations out of Charleston Harbor near Coos Bay, Oregon. The cruise plan consisted of 23 days on site split between sonar mapping and video ground truth surveying. Activities parsed out to nine days of sonar mapping, three days of video surveying, eight ... |
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Video-OCS Floating Wind Farm Site
This data release contains digital video files from the USGS field activity 2014-607-FA, a survey of the Oregon Outer Continental Shelf (OCS) Floating Wind Farm Site in 2014. Video data were collected over 3 days between September 6 and September 9, 2014 using a towed camera sled system. 11.6 hours of video were collected along 18 transects; the mean length of time per transect was 38 minutes. Video operations were conducted by deploying up drift of a target and drifting over it at speeds of 1 knot or ... |
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Structure-from-Motion bathymetric maps from the Florida Keys, 2019
Structure-from-Motion (SfM) bathymetric maps were created using seafloor images collected using the new 5-camera system SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). Images were collected during July 2019 by towing the SQUID-5 in 3 to 4 meters of water off of Islamorada in the Florida Keys during 3 days. The five cameras were synchronized together and with a survey-grade Global Positioning System (GPS). Images were collected over diverse benthic settings, including living and ... |
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Structure-from-Motion orthophotos from the Florida Keys, 2019
Georeferenced orthophotos were created from structure-from-motion (SfM) data using seafloor images collected using the new 5-camera system SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). Images were collected in July 2019 by towing the SQUID-5 in 3 to 4 meters of water off of Islamorada in the Florida Keys during 3 days. The five cameras were synchronized together and with a survey-grade Global Positioning System (GPS). Images were collected over diverse benthic settings, including ... |
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Structure-from-Motion point clouds from the Florida Keys, 2019
Structure-from-Motion (SfM) point clouds were created from seafloor images collected using the new 5-camera system SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). Images were collected in July 2019 by towing the SQUID-5 in 3 to 4 meters of water off of Islamorada in the Florida Keys during 3 days. The five cameras were synchronized together and with a survey-grade Global Positioning System (GPS). Images were collected over diverse benthic settings, including living and senile reefs, ... |
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Structure-from-Motion underwater photos from the Florida Keys, 2019
Underwater photos were collected using a new 5-camera system, the Structure-from-Motion (SfM) Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). Images were collected in July 2019 by towing the SQUID-5 in 3 to 4 meters of water off of Islamorada in the Florida Keys. The five cameras were synchronized together and with a survey-grade Global Positioning System (GPS). Images were collected over diverse benthic settings, including living and senile reefs, rubble, and sand. The images are ... |
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Elevation data collected in 2009 on the beach and foreshore in the vicinity of Wainwright, Alaska
Beach and foreshore elevation data were collected in the vicinity of Wainwright, Alaska. The area from the mouth of the Kuk River to about 8 km to the northeast was measured in August 2009. The area from the mouth of the Kuk River to about 5 km to the northeast was measured in October 2009. The elevation data were collected with Real-Time Kinematic (RTK) Global Positioning System (GPS) systems mounted on all-terrain vehicles. The GPS sampling rate was 1 Hz with vehicle speeds maintained at less than 15 km ... |
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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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2022-334-FA_BocaChica_Polecam_Imagery: High-resolution Geophysical and Imagery Data Collected in November 2022 Offshore of Boca Chica Key, FL
Underwater images totaling 23,948 in number were collected offshore of Boca Chica Key, the Florida Keys , during November 2022, using the Polecam system developed by the U.S. Geological Survey (USGS). The Polecam system is a single downward-looking underwater Teledyne FLIR camera with a Computar V1228-MPY lens and with a rigidly-attached Global Navigation Satellite System (GNSS) antenna mounted directly above the camera's central axis. The pole camera was attached to the gunwale of the USGS research vessel ... |
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2023-310-FA_Oahu_Polecam_Imagery: High-resolution Geophysical and Imagery Data Collected in May 2023 Near Fort Hase, Marine Corps Base Hawaii
Underwater images totaling 78,924 in number were collected offshore Fort Hase, Marine Corps Base Hawaii (MCBH) and Coconut Island, Oahu, Hawaii, during May 2023, using the Polecam system developed by the U.S. Geological Survey (USGS). The Polecam system is a single downward-looking underwater Teledyne FLIR camera with a Computar V1228-MPY lens and with a rigidly-attached Global Navigation Satellite System (GNSS) antenna mounted above and forward of the camera's central axis. The Polecam system captured ... |
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CoconutIsland_2023_MBES: High-resolution Geophysical and Imagery Data Collected in May 2023 Near Fort Hase, Marine Corps Base Hawaii
An Ellipsoidally Referenced Survey (ERS) using a Norbit Winghead multibeam echosounder, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) nearshore Coconut Island, on the island of Oahu, May 7, 2023. This dataset, CoconutIsland_2023_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 0.25 meter (m) bathymetric grid and the dataset CoconutIsland_2023_MBES_Backscatter.zip includes the acoustic backscatter intensity ... |
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FtHase_2023_MBES: High-resolution Geophysical and Imagery Data Collected in May 2023 Near Fort Hase, Marine Corps Base Hawaii
An Ellipsoidally Referenced Survey (ERS) using a Norbit Winghead multibeam echosounder, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) nearshore Fort Hase Marine Corps Base Hawaii (MCBH), on the island of Oahu, May 4-12, 2023. This dataset, FtHase_2023_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid and the dataset FtHase_2023_MBES_Backscatter.zip includes the acoustic backscatter ... |
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Acoustic-backscatter data for Jenkinson Lake, California collected during USGS field activity 2022-604-FA
Here January 2022 1-m resolution acoustic-backscatter data are provided for Jenkinson Lake, California. Acoustic-backscatter data were collected during three separate SWATHPlus surveys of Jenkinson Lake. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity numbers 2022-604-FA (January 2022), 2022-649-FA (August 2022), and 2023-634-FA (July 2023). Data are provided as a GeoTIFF image. |
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Acoustic-backscatter data for Jenkinson Lake, California collected during USGS field activity 2022-649-FA
Here August 2022 1-m resolution acoustic-backscatter data are provided for Jenkinson Lake, California. Acoustic-backscatter data were collected during three separate SWATHPlus surveys of Jenkinson Lake. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity numbers 2022-604-FA (January 2022), 2022-649-FA (August 2022), and 2023-634-FA (July 2023). Data are provided as a GeoTIFF image. |
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Acoustic-backscatter data for Jenkinson Lake, California collected during USGS field activity 2023-634-FA
Here July 2023 1-m resolution acoustic-backscatter data are provided for Jenkinson Lake, California. Acoustic-backscatter data were collected during three separate SWATHPlus surveys of Jenkinson Lake. Data were collected and processed by the the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity numbers 2022-604-FA (January 2022), 2022-649-FA (August 2022), and 2023-634-FA (July 2023). Data are provided as a GeoTIFF image. |
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Bathymetry data for Jenkinson Lake, California collected during USGS field activity 2022-604-FA
Here January 2022 1-m resolution bathymetry data of Jenkinson Lake, California are provided for the entire lake and 0.5-m resolution bathymetry data are provided for the shallower upper basin. Bathymetry data were collected during three separate SWATHPlus surveys of Jenkinson Lake. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity numbers 2022-604-FA (January 2022), 2022-649-FA (August 2022), and 2023-634-FA ... |
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Bathymetry data for Jenkinson Lake, California collected during USGS field activity 2022-649-FA
Here August 2022 1-m resolution bathymetry data of Jenkinson Lake, California are provided for the entire lake and 0.5-m resolution bathymetry data are provided for the shallower upper basin. Bathymetry data were collected during three separate SWATHPlus surveys of Jenkinson Lake. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity numbers 2022-604-FA (January 2022), 2022-649-FA (August 2022), and 2023-634-FA (July ... |
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Bathymetry data for Jenkinson Lake, California collected during USGS field activity 2023-634-FA
Here July 2023 1-m resolution bathymetry data of Jenkinson Lake, California are provided for the entire lake and 0.5-m resolution bathymetry data are provided for the shallower upper basin. Bathymetry data were collected during three separate SWATHPlus surveys of Jenkinson Lake. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity numbers 2022-604-FA (January 2022), 2022-649-FA (August 2022), and 2023-634-FA (July ... |
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Conductivity, temperature and salinity time-series data collected in 2009 in the vicinity of Wainwright, Alaska
Measurements of conductivity and temperature were collected with a high-accuracy conductivity and temperature recorder (Seabird SBE37 microcat) in approximately 10 m water depth at a single location fronting the village of Wainwright, Alaska, from 24 August to 1 October 2009. The instrument was mounted on the frame approximately 0.50 m off the bottom of the seafloor. Salinity was calculated from conductivity measurements. |
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Conductivity, temperature, depth, salinity, dissolved oxygen, nitrogen, and fluorescence data collected in 2009 in the vicinity of Wainwright, Alaska
Measurements of conductivity, temperature, and depth (CTD), in addition to dissolved oxygen, nitrogen, and fluorescence, were collected in the Wainwright Inlet, the mouth of the Kuk River, and in the nearshore region off Wainwright, Alaska, in August 2009 with a Seabird SBE 19. Post-survey calculations of salinity were made from the conductivity measurements. |
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Current profiler time-series data collected in 2009 offshore of Wainwright, Alaska
A time-series of binned current-velocities and recorded ping amplitudes were collected offshore Wainwright, Alaska, from August 24 to October 02, 2009 (UTC). Measurements were collected using a 1 MHz NortekTM AWAC acoustic Doppler current profiler mounted on a frame in approximately 10 m of water. The profiler was mounted on the frame 0.55 m off the bottom of the seafloor, and collected data in 8 vertical bins, centered at 1.95(bin1), 2.95, 3.95, 4.95, 5.95, 6.95, 7.95, and 8.95(bin8) meters above the ... |
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Grain-size data from samples collected in 2009 at three bluff locations in the vicinity of Wainwright, Alaska
Grain-size data, including grain size, cumulative frequency, and median, was determined from samples collected at three bluff locations in the vicinity of Wainwright, Alaska. The different locations (termed W1, W2, and W3) represent three different slope-failure modes. Sediment grab samples for most bluff locations were collected in August 2009, while sediment samples from the bluff at site W3 and multiple foreshore locations were collected in September and October of 2009. Two of the grab samples were ... |
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Grain-size distribution from foreshore and beach samples collected in 2009 in the vicinity of Wainwright, Alaska
Sediment grab samples were collected at 158 locations on the foreshore and beach in the vicinity of Wainwright, Alaska, in October of 2009. Two of the grab samples were sieved and analyzed for grain size distributions. The grain-size distribution of the remaining samples was determined from referenced photographs of collected samples taken in the lab following the method of Barnard and others (2007) using two-dimensional spectral decomposition of sediment images (Buscombe and others, 2010). Results of ... |
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Water depth time-series data collected in 2009 offshore of Wainwright, Alaska
A time-series of varying water depths were collected offshore of Wainwright, Alaska, from August 23 to October 02, 2009 (UTC). Measurements were collected with a built-in pressure transducer from a 1 MHz NortekTM AWAC acoustic Doppler current profiler mounted on a frame in approximately 10 m of water. The instrument was mounted to the frame at 0.55 m off the bottom of the seafloor. Reported depth values include the 0.55 m offset, and thus are depths relative to the seabed. These data are available in a ... |
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Wave time-series data collected in 2009 offshore of Wainwright, Alaska
Time series wave data were collected offshore of Wainwright, Alaska, from August 24 to October 02, 2009 (UTC). Measurements were collected using a 1 MHz NortekTM AWAC acoustic Doppler current profiler mounted on a frame in approximately 10 m of water. The instrument was mounted to the frame at 0.55 m off the bottom of the seafloor, and collected data in 8.53-minute bursts at 2 Hz. Significant wave heights (Hs), maximum significant wave heights (Hmax), peak and mean wave periods (Tp and Tm, respectively), ... |
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High-resolution acoustic backscatter data collected southwest of Chenega Island, Alaska during field activity 2014-622-FA
High-resolution acoustic backscatter data were collected by the U.S. Geological Survey (USGS) and the Alaska Department of Fish and Game in May of 2014 southwest of Chenega Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during USGS field activity 2014-622-FA, using a pole mounted 100-kHz Reson 7111 multibeam echosounder. |
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High-resolution acoustic backscatter data collected southwest of Montague Island, Alaska during field activity 2014-622-FA
High-resolution acoustic backscatter data were collected by the U.S. Geological Survey (USGS) and the Alaska Department of Fish and Game in May of 2014 southwest of Montague Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during USGS field activity 2014-622-FA, using a pole mounted 100-kHz Reson 7111 multibeam echosounder. |
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High-resolution multibeam bathymetry data collected southwest of Chenega Island, Alaska during field activity 2014-622-FA
High-resolution multibeam data were collected by the U.S. Geological Survey (USGS) and the Alaska Department of Fish and Game in May of 2014 southwest of Chenega Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during USGS field activity 2014-622-FA, using a pole mounted 100-kHz Reson 7111 multibeam echosounder. |
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High-resolution multibeam bathymetry data collected southwest of Montague Island, Alaska during field activity 2014-622-FA
High-resolution multibeam data were collected by the U.S. Geological Survey (USGS) and the Alaska Department of Fish and Game in May of 2014 southwest of Montague Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during USGS field activity 2014-622-FA, using a pole mounted 100-kHz Reson 7111 multibeam echosounder. |
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Minisparker seismic-reflection data collected southwest of Montague Island and southwest of Chenega, Alaska during field activity 2014-622-FA
High-resolution single channel minisparker seismic-reflection data were collected by the U.S. Geological Survey and the Alaska Department of Fish and Game in May 2014 in southern Prince William Sound southwest of Chenega and from southwest of Montague Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during field activity 2014-622-FA, using a 500 Joule SIG 2-mille minisparker sound source and a single channel streamer and recorded with a Triton SB-Logger ... |
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Multichannel minisparker seismic-reflection data collected offshore Glacier Bay National Park during USGS field activity 2015-629-FA
Multichannel seismic reflection data were collected by the U.S. Geological Survey in May of 2015 outside of Palma Bay, Alaska. Seismic data were acquired coincidentally with high resolution bathymetry (Dartnell and others, 2022). |
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Chirp sub-bottom data collected offshore Northern California during USGS field activity 2019-643-FA
Chirp sub-bottom data were collected by the U.S. Geological Survey in 2019 offshore Humboldt County of northern California to expand data coverage along the southern Cascadia Margin. |
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Multichannel minisparker seismic-reflection data collected offshore Northern California during USGS field activity 2019-643-FA
High-resolution multichannel seismic (MCS) data were collected by the U.S. Geological Survey in 2019 offshore Humboldt County of northern California to expand data coverage along the southern Cascadia Margin. |
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Information on sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset presents core information such as core IDs, section numbers, lengths, depth intervals, and locations from sediment cores collected in southern Cascadia (offshore northern California) aboard the M/V Bold Horizon in September-October 2019. An inventory of core section CT, MSCL, and photograph scan files available in this data release are listed here. |
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Computed tomography (CT) scans of sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset includes computed tomography (CT) scan imagery of sediment cores collected in southern Cascadia (offshore northern California) aboard the M/V Bold Horizon in September-October 2019. |
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Multi-sensor core logger (MSCL) scans of sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset includes multi-sensor core logger (MSCL) data of sediment cores collected in southern Cascadia (offshore northern California) aboard the M/V Bold Horizon in September-October 2019. |
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Photographs of sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset includes photographs (linescan images) of sediment cores collected in southern Cascadia (offshore northern California) aboard the MV Bold Horizon in September-October 2019. |
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Radiocarbon age data from sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset presents radiocarbon data from 87 samples from sediment cores collected in southern Cascadia (offshore northern California) aboard the M/V Bold Horizon in September-October 2019. Sample ages were determined by the National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility and the W.M. Keck Carbon Cycle Accelerator Mass Spectrometry (KCCAMS) facility at the University of California, Irvine (UCI). |
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