Automated Building Damage Detection Post Hurricane: Enhancing Accuracy using Hybrid Ensemble Model

One of the most destructive natural disasters, hurricanes destroy a great deal of property, harm people, and disrupt the ecosystem and world economy. Earlier, through ground surveys, the impact of the hurricane was measured, by locating the damaged and undamaged infrastructure in the affected area, which is a very time-consuming and labor-intensive method. Damage detection is carried out for rapid response and recovery. A fast and enhanced method is proposed in this study that uses a hybrid ensemble model on the post-hurricane satellite images of the 2017 Hurricane Harvey in Greater Houston. The structured approach that includes data handling, model development, and image preparation is introduced. Leveraging a hybrid ensemble model combining EfficientNetB0, VGG19, and InceptionResNetV2, the proposed model is built, trained, fine-tuned, and tested, achieving improved accuracy of 93%, 90% precision, 99% recall, 85% AUC, and 95% F1-score in automated damage detection. This framework not only accelerates disaster recovery but also offers valuable insights for future resilience planning.