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Sentinel-1 imagery data is made available in Skylight
The Sentinel-1 Model is based upon the classic object detection model, Faster R-CNN, which is a two stage detector made up of a Region Proposal Network (RPN) and a classification head. The RPN’s job is to generate proposals, where there is likely an object of interest, and the classification head grabs the most confident of these proposals and predicts scores (such as is_vessel and is_fishing) for each proposal.
Model
The current version of the model has been trained on Sentinel-1 scenes from several geographic areas that we annotated by hand. These areas include water bodies around Bahamas, Ghana, Madagascar, Persian Gulf, Argentina, Taiwan, and Singapore. The model predicts the positions of vessels, and assigns each prediction a confidence score. The only other attribute that the model predicts is vessel length. Specifically, the Docker container will take in decompressed Sentinel-1 scenes, and save the crops of vessels detected in those scenes.
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The model is trained on data that includes overlapping images captured at different times. It will compare the images to distinguish moving ships from static objects like islands, platforms. and other static non-vessel structures.
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