Post-earthquake damage assessment of buildings exploiting data fusion

Post-earthquake damage assessment is a critical step in disaster response and recovery. This paper introduces an innovative approach leveraging the EfficientNet-B0 neural network combined with wavelet-based data fusion (early and intermediate fusion strategies) to classify earthquake-related damage to existing buildings. The classification framework distinguishes between three damage levels: slight, moderate, and severe, in line with the standards defined by the Italian AeDES manual. The core innovation lies in the creation of a robust and expertly validated database, derived from a restructured and enhanced version of the PHI-Net dataset and a newly curated dataset based on the Macroseismic Photographic Database (DFM) from the Italian National Institute of Geophysics and Volcanology (INGV). This last dataset incorporates samples from various documented Italian earthquakes. The proposed method aims at supporting field operators by automating the damage classification step, thus streamlining the compilation of AeDES forms. Results demonstrate that the proposed method achieves a higher accuracy and reliability, compared to other state-of-the-art strategies, offering a significant contribution to automated seismic damage assessment.