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Fully understanding a complex high-resolution satellite or aerial imageryscene often requires spatial reasoning over a broad relevant context. The humanobject recognition system is able to understand object in a scene over along-range relevant context. For example, if a human observes an aerial scenethat shows sections of road broken up by tree canopy, then they will beunlikely to conclude that the road has actually been broken up into disjointpieces by trees and instead think that the canopy of nearby trees is occludingthe road. However, there is limited research being conducted to understandlong-range context understanding of modern machine learning models. In thiswork we propose a road segmentation benchmark dataset, Chesapeake Roads SpatialContext (RSC), for evaluating the spatial long-range context understanding ofgeospatial machine learning models and show how commonly used semanticsegmentation models can fail at this task. For example, we show that a U-Nettrained to segment roads from background in aerial imagery achieves an 84%recall on unoccluded roads, but just 63.5% recall on roads covered by treecanopy despite being trained to model both the same way. We further analyze howthe performance of models changes as the relevant context for a decision(unoccluded roads in our case) varies in distance. We release the code toreproduce our experiments and dataset of imagery and masks to encourage futureresearch in this direction -- https://github.com/isaaccorley/ChesapeakeRSC.

Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery