Seg2Change adapts open-vocabulary segmentation models to open-vocabulary change detection via a category-agnostic change head and new dataset CA-CDD, delivering +9.52 IoU on WHU-CD and +5.50 mIoU on SECOND.
Xu, Wenhan Lu, Zebo Li, Pranav Khaitan, and Valeriya Zaytseva
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A Perceiver IO fusion architecture combines satellite and street-level imagery via DINOv2 tokens and RGB-M masking to classify roof attributes on a new dataset of 32,135 buildings across ten countries.
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Seg2Change: Adapting Open-Vocabulary Semantic Segmentation Model for Remote Sensing Change Detection
Seg2Change adapts open-vocabulary segmentation models to open-vocabulary change detection via a category-agnostic change head and new dataset CA-CDD, delivering +9.52 IoU on WHU-CD and +5.50 mIoU on SECOND.
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Multi-Modal Building Inspection via Perceiver IO Fusion of Satellite and Street-Level Imagery
A Perceiver IO fusion architecture combines satellite and street-level imagery via DINOv2 tokens and RGB-M masking to classify roof attributes on a new dataset of 32,135 buildings across ten countries.