Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation
March 18, 2022
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, Atlantic, and Great Lakes coastlines, consisting of time-series of high-resolution (
Citation Information
| Publication Year | 2022 |
|---|---|
| Title | Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation |
| DOI | 10.5066/P91NP87I |
| Authors | Phillipe A Wernette, Daniel D Buscombe, Jaycee Favela, Sharon N Fitzpatrick, Evan Goldstein, Nicholas M Enwright, Erin Dunand |
| Product Type | Data Release |
| Record Source | USGS Asset Identifier Service (AIS) |
| USGS Organization | Pacific Coastal and Marine Science Center |
| Rights | This work is marked with CC0 1.0 Universal |
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