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.
The neural city: A next-generation spatio-temporal intelligence paradigm for urban holistic governance.The Innovation, 7(2), 2026
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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.