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arXiv · 2204.01807

Revisiting Near/Remote Sensing with Geospatial Attention

Scott Workman·M. Usman Rafique·Hunter Blanton·Nathan Jacobs
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Abstract

This work addresses the task of overhead image segmentation when auxiliary ground-level images are available. Recent work has shown that performing joint inference over these two modalities, often called near/remote sensing, can yield significant accuracy improvements. Extending this line of work, we introduce the concept of geospatial attention, a geometry-aware attention mechanism that explicitly considers the geospatial relationship between the pixels in a ground-level image and a geographic location. We propose an approach for computing geospatial attention that incorporates geometric features and the appearance of the overhead and ground-level imagery. We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks. The results demonstrate that our method significantly outperforms the previous state-of-the-art methods.

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Scott Workman, M. Usman Rafique, Hunter Blanton, Nathan Jacobs. 2022-04-04. Revisiting Near/Remote Sensing with Geospatial Attention. https://arxiv.org/abs/2204.01807

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