DISASTER DAMAGE ESTIMATION USING SATELLITE IMAGERY AND MACHINE LEARNING

Abstract

This research presents a comprehensive framework for disaster damage estimation using multispectral satellite imagery and machine learning models. The system leverages Google Earth Engine (GEE) for geospatial data collection and employs deep learning algorithms to classify the extent of damage in affected areas. In addition to classification, the model quantifies the estimated economic loss in INR, providing actionable insights for post-disaster management and recovery. The pipeline integrates preprocessing, segmentation, feature extraction, and INR conversion to deliver accurate, scalable, and real-time assessments for both natural and man-made disasters.