MSBDA-Net: Multi-scale Siamese Building Damage Assessment Network

Abstract

During or after natural disasters, information about location, cause, and severity, is crucial for early responders to act accordingly. Building damage is one of the major disaster types that occurred repeatedly. Being able to estimate the extent and location of damaged buildings are important so that emergency personnel and rescue teams can expedite efforts to the right building in affected location. Satellite imagery is a powerful visual resource that can be used to assess the extent of damages within a wide geographical area. However, current post-disaster practice requires manual annotation of damaged buildings, which is labor intensive and time consuming. Resultantly, traditional damage detection methods have been outperformed in terms of accuracy by Deep Learning (DL) architectures such as the Convolutional Neural Networks (CNN). Therefore, we developed a novel framework named Multi-scale Siamese Building Damage Assessment Network (MSBDA-Net). The proposed framework includes a two-step approach. The first stage is building localization, which a mask of all buildings before disaster will be generated. The second stage is a multi-scale Siamese damage assessment model, where the network takes the image pairs contained pre- and post-disaster as input and classify building on different damage levels. The evaluation results of proposed method indicate the applicability of the proposed method in both building segmentation (Fl-score=86.3%) and damage assessment (Fl-score=78.44 %)