Automated building damage assessment and large-scale mapping by integrating satellite imagery, GIS, and deep learning

Abstract

Efficient and accurate building damage assessment is crucial for effective emergency response and resource allocation following natural hazards. However, traditional methods are often time consuming and labor intensive. Recent advancements in remote sensing and artificial intelligence (AI) have made it possible to automate the damage assessment process, and previous studies have made notable progress in machine learning classification. However, the application in postdisaster emergency response requires an end-to-end model that starts with satellite imagery as input and automates the generation of large-scale damage maps as output, which was rarely the focus of previous studies. Addressing this gap, this study integrates satellite imagery, Geographic Information Systems (GIS), and deep learning. This enables the creation of comprehensive, large-scale building damage assessment maps, providing valuable insights into the extent and spatial variation of damage. The effectiveness of this methodology is demonstrated in Galveston County following Hurricane Ike, where the classification of a large ensemble of buildings was automated using deep learning models trained on the xBD data set. The results showed that utilizing GIS can automate the extraction of subimages with high accuracy, while fine-tuning can enhance the robustness of the damage classification to generate highly accurate large-scale damage maps. Those damage maps were validated against historical reports.