Revisiting Near/Remote Sensing with Geospatial Attention
This work addresses the problem of multimodal image segmentation for geospatial analysis, representing an incremental improvement over existing near/remote sensing methods.
This paper tackles overhead image segmentation with auxiliary ground-level images by introducing geospatial attention, a geometry-aware attention mechanism that explicitly models geospatial relationships between ground-level pixels and geographic locations. The method significantly outperforms previous state-of-the-art approaches on five segmentation tasks.
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.