Smart Classification of Damaged Buildings through Remote Sensing

This study proposes a framework for semi-automated assessment of building damage caused by earthquakes using remote sensing imagerydatasets, combined with advanced machine learning techniques. The framework uses high-resolution post-event InSAR images. A machine learning approach is employed to classify the damage states of buildings in earthquakeaffected areas. Multi-class damage classification are performed for earthquake events. The binary classification successfully identified over fifty percent of damaged buildings in previous studies. The multi-class damage classification using InSAR data represents a relatively novel application, and the case studies presented highlight one of the first such efforts to leverage InSAR imagery for building-level damage assessment using CNN model