Scale-aware Recognition in Satellite Images under Resource Constraints

Recognition of features in satellite imagery (forests, swimming pools, etc.) depends strongly on the spatial scale of the concept and therefore the resolution of the images. This poses two challenges: Which resolution is best suited for recognizing a given concept, and where and when should the costlier higher-resolution (HR) imagery be acquired? We present a novel scheme to address these challenges by introducing three components: (1) A technique to distill knowledge from models trained on HR imagery to recognition models that operate on imagery of lower resolution (LR), (2) a sampling strategy for HR imagery based on model disagreement, and (3) an LLM-based approach for inferring concept "scale". With these components we present a system to efficiently perform scale-aware recognition in satellite imagery, improving accuracy over single-scale inference while following budget constraints. Our novel approach offers up to a 26.3% improvement over entirely HR baselines, using 76.3% fewer HR images. Resources are available on our website. Figure 1 : With these images we can see how concept scale is linked to spatial resolution. If we are seeking out a spatially large concept like forest, lower resolutions are favored (b), as higher resolutions may lack the needed context to discern between a forest (a) and a park (c). At the same time while seeking out finer concepts such as sports track, certain details can only be discerned well at higher resolutions (d) and are obscured at lower resolutions (e).