The U.S. Geological Survey deployed small uncrewed aircraft systems (sUAS) to collect aerial remote sensing data across sites within the Lower Darby Creek Superfund Site and the adjacent John Heinz National Wildlife Refuge (JHNWR) ~5 miles outside of Philadelphia, PA in March and August of 2024. March datasets include aerial images from natural color (RGB) and thermal infra-red (TIR) sensors across the JHNWR and adjacent tributaries as well as the nearby Clearview Landfill within the superfund site. ...

This data release serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery from Sabine Pass to Bay Coquette, Louisiana (LA). A total of 179 images acquired between 2013 and 2024 were analyzed. Water, bare earth (sand), and vegetated land-cover classes were mapped using (1) successive thresholding and masking of the modified normalized difference water index (mNDWI), the normalized difference bare land index (NBLI), and the normalized difference vegetation index ...

Small Uncrewed Aircraft Systems (sUAS) were used to collect aerial remote sensing data over Sesuit Marsh in Dennis, MA. Raw data from aerial surveys include aerial images from natural color (RGB) and multispectral cameras and raw lidar data. These datasets were processed to produce high resolution digital elevation models (DEM), image mosaics, and lidar point clouds (LPC) to provide local partners with high resolution imagery and elevation data to monitor and identify vegetation cover of a salt marsh on ...

The data release (Bernier, 2021) associated with this metadata record serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery at the northern Chandeleur Islands, Louisiana. To minimize the effects of tidal water-level variations, 75 cloud-free, low-water images acquired between 1984 and 2019 were analyzed. Water, bare earth (sand), vegetated, and intertidal land-cover classes were mapped from Hewes Point to Palos Island using successive thresholding and masking ...

This data release serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery from Delaware Bay, New Jersey (NJ) to Shinnecock Bay, New York (NY). A total of 119 images acquired between 2008 and 2022 were analyzed to produce 143 thematic land-cover raster datasets. Water, bare earth (sand), and vegetated land-cover classes were mapped using successive thresholding and masking of the modified normalized difference water index (mNDWI), the normalized difference bare ...

The data release (Bernier, 2021) associated with this metadata record serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery at the northern Chandeleur Islands, Louisiana. To minimize the effects of tidal water-level variations, 75 cloud-free, low-water images acquired between 1984 and 2019 were analyzed. Water, bare earth (sand), vegetated, and intertidal land-cover classes were mapped from Hewes Point to Palos Island using successive thresholding and masking ...

This data release serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery from Delaware Bay, New Jersey (NJ) to Shinnecock Bay, New York (NY). A total of 119 images acquired between 2008 and 2022 were analyzed to produce 143 thematic land-cover raster datasets. Water, bare earth (sand), and vegetated land-cover classes were mapped using successive thresholding and masking of the modified normalized difference water index (mNDWI), the normalized difference bare ...

This data release serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery from Sabine Pass to Bay Coquette, Louisiana (LA). A total of 179 images acquired between 2013 and 2024 were analyzed. Water, bare earth (sand), and vegetated land-cover classes were mapped using (1) successive thresholding and masking of the modified normalized difference water index (mNDWI), the normalized difference bare land index (NBLI), and the normalized difference vegetation index ...

Coast Train is a library of images of coastal environments, annotations, and corresponding thematic label masks (or ‘label images’) collated for the purposes of training and evaluating machine learning (ML), deep learning, and other models for image segmentation. It includes image sets from both geospatial satellite, aerial, and UAV imagery and orthomosaics, as well as non-geospatial oblique and nadir imagery. Images include a diverse range of coastal environments from the U.S. Pacific, Gulf of Mexico, ...