Machine Learning Approach for Catastrophe Risk Assessment and Management Using Remote Sensing Data

Abstract

With emergency programs for disaster preparedness and warning phases for earthquakes, landslides, and floods in recent years, remote sensing and geographic information systems have played a crucial role in Catastrophe Risk assessment and management. It has also been a key focus in the field of technology. Without the right tool for organizing massive volumes of data and gathering information from many sources, such maps or measurement channels, it would not be feasible to employ sensory data. In order to identify and assess areas affected by floods, earthquakes, avalanches, landslides, and wildfires, this study employs machine learning approaches. Following the application of filters to enhance image quality, the images are segmented through thresholding technique and classified using supervised and unsupervised classification methods. Images from before and after disasters are gathered from MRSAC Nagpur and processed using Python-based tools, ArcGIS, ERDAS, and QGIS for the purpose of analyzing devastation.