ChesapeakeRSC (Chesapeake Roads Spatial Context)
Introduced by Robinson et al. in Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery
A novel remote sensing dataset for evaluating a geospatial machine learning model's ability to learn long range dependencies and spatial context understanding. We create a task to use as a proxy for this by training models to extract roads which have been broken into disjoint pieces due to tree canopy occluding large portions of the road.
The dataset consists of 30,000 RGBN NAIP images and land cover annotations from the Chesapeake Conservacy containing significant amounts of the Tree Canopy Over Road category.
Papers
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