Satellite to Street : Disaster Impact Estimator
Sreesritha Sai, Sai Venkata Suma Sreeja, Sai Sri Deepthi, Nikhil

TL;DR
This paper presents a deep-learning framework that produces detailed damage maps from satellite images before and after disasters, improving accuracy and speed over traditional methods.
Contribution
It introduces a dual-input U-Net architecture with class-aware loss for better damage detection in imbalanced datasets, advancing automated disaster assessment.
Findings
Outperforms conventional segmentation networks in damage classification
Accurately distinguishes different severity levels of damage
Provides faster, objective damage analysis
Abstract
Accurate assessment of post-disaster damage is essential for prioritizing emergency response, yet current practices rely heavily on manual interpretation of satellite imagery.This 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…
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Taxonomy
TopicsRemote-Sensing Image Classification · Advanced Neural Network Applications · Flood Risk Assessment and Management
