[2512.00065] Satellite to Street : Disaster Impact Estimator

Computer Science > Computer Vision and Pattern Recognition arXiv:2512.00065 (cs) [Submitted on 24 Nov 2025 (v1), last revised 21 Mar 2026 (this version, v3)] Title:Satellite to Street : Disaster Impact Estimator Authors:Sreesritha Sai, Sai Venkata Suma Sreeja, Sai Sri Deepthi, Nikhil View a PDF of the paper titled Satellite to Street : Disaster Impact Estimator, by Sreesritha Sai and 3 other authors View PDF HTML (experimental) Abstract:Accurate assessment of post-disaster damage is essential for prioritizing emergency response, yet current practices rely heavily on manual interpretation of satellite this http URL approach is time-consuming, subjective, and difficult to scale during large-area disasters. Although recent deep-learning models for semantic segmentation and change detection have improved automation, many of them still struggle to capture subtle structural variations and often perform poorly when dealing with highly imbalanced datasets, where undamaged buildings dominate. This thesis introduces Satellite-to-Street:Disaster Impact Estimator, a deep-learning framework that produces detailed, pixel-level damage maps by analyzing pre and post-disaster satellite images together. The model is built on a modified dual-input U-Net architecture that strengthens feature fusion between both images, allowing it to detect not only small, localized changes but also broader contextual patterns across the scene. To address the imbalance between damage categories, a class-aware...