S2M extracts structured text quadruples from change masks to provide noise-free multimodal supervision, achieving 17.80% Sek and 66.14% F_scd on the new Gaza-Change-v2 dataset and outperforming LLM-based multimodal methods.
Changemamba: Remote sensing change detection with spatiotemporal state space model.IEEE Transactions on Geoscience and Remote Sensing, 62:1–20
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DPG-CD uses an estimated depth prior from imagery, gated fusion, and multi-stage cross-modal architecture to jointly predict 2D semantic and 3D height changes, outperforming prior methods on Hi-BCD, 3DCD, and NYC-MMCD datasets.
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Masks Can Talk: Extracting Structured Text Information from Single-Modal Images for Remote Sensing Change Detection
S2M extracts structured text quadruples from change masks to provide noise-free multimodal supervision, achieving 17.80% Sek and 66.14% F_scd on the new Gaza-Change-v2 dataset and outperforming LLM-based multimodal methods.
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DPG-CD: Depth-Prior-Guided Cross-Modal Joint 2D-3D Change Detection
DPG-CD uses an estimated depth prior from imagery, gated fusion, and multi-stage cross-modal architecture to jointly predict 2D semantic and 3D height changes, outperforming prior methods on Hi-BCD, 3DCD, and NYC-MMCD datasets.