Post-Disaster Change Detection with Fusion-Based CNN Models

Abstract

Post-disaster change detection plays a crucial role in emergency response and urban damage assessment. This study introduces four CNN-based encoder-decoder models designed with distinct feature fusion strategies: early fusion, middle fusion, late fusion, and an improved middle fusion model integrated with a custom upsampling module. All models were trained and tested on the TUE-CD dataset, which includes pre- and post-disaster satellite images from the 2023 Türkiye earthquake. Among the models, the middle fusion approach achieved the best overall performance by combining intermediate-level features from bitemporal inputs. The enhanced model further improved segmentation accuracy by preserving spatial detail. Results indicate that the choice of fusion level significantly affects model performance and generalization. Middle fusion, in particular, offers a promising solution for reliable and accurate change detection in disaster scenarios. Future work may focus on integrating more efficient architectures and evaluating performance across different types of disasters. © 2025 IEEE.