Sentinel-1 is a satellite managed by the European Space Agency. Read about the use of imagery from Sentinel-1 in Skylight here.
Model
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.
The model detects vessels and predicts vessel attributes from Sentinel-1 SAR images. In particular, it uses the dual polarization mode (VV + VH) of the Interferometric Wide swath (IW) acquisition mode of Sentinel-11, and produces point detections of vessels, cropped outputs surrounding those detections, and attributes associated with the detected vessel; currently estimated length is displayed in Skylight).
Training Data
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.
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.