Omer Aviv

Framework for Cross-Disaster Building Damage Assessment Using Cost-Sensitive Learning

A unified deep learning framework for building damage assessment from satellite imagery that integrates segmentation-driven feature construction, morphological processing, cost-sensitive learning, and cross-disaster evaluation to support robust performance under limited and imbalanced data conditions is presented.