Deep Ensemble Learning for Rapid Large-Scale Post-Earthquake Damage Assessment---Application to 2023 Kahramanmaraş Earthquake Sequence

Abstract

Abstract Extensive impacted region and government damage surveys following the 2023 Kahramanmaraş earthquake sequence provided a unique opportunity to design and validate a rapid post-event damage assessment framework with satellite images, addressing the typical challenges (e.g., generalization to unseen areas) encountered in rapid regional assessment efforts. By analyzing images from the significantly impacted city of Antakya, we manually identified visual features and compared those with damage states in the survey. Then, an Artificial Intelligence-based framework was developed to automate segmentation and damage assessment processes for future test areas. The resulting tool, dubbed RAPID-A, is an uncertainty-aware ensemble of various deep segmentation models fed with public data released shortly after such incidents. Furthermore, the extent of the damages reported in the survey allowed us to propose and validate a strategy to identify various intensities of damage using a single model, considering the less severe damage as more uncertain. Test case studies in Gaziantep and Kahramanmaraş demonstrate that RAPID-A is generalizable, accurate, and efficient. The significance of this framework lies in its ability to assist in rapid initial damage evaluations before expert dispatch or to replace it in countries that lack the capacity to launch large-scale regional assessment programs to guide foreign support.