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