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Found 2 papers, 2 papers with code
Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks
In the aftermath of disasters, building damage maps are obtained using change detection to plan rescue operations.
BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge
While convolutional neural networks are at the core of recent change detection solutions, we present in this work, BLDNet, a novel graph formulation for building damage change detection and enable learning relationships and representations from both local patterns and non-stationary neighborhoods.
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