Application of MambaBDA for Building Damage Assessment in the 2025 Los Angeles Wildfire

Timely detection of the spatial distribution of building damage in the immediate aftermath of a disaster is essential for guiding emergency response and recovery strategies. In January 2025, a large-scale wildfire struck the Los Angeles metropolitan area, with Altadena as one of the most severely affected regions. A timely and comprehensive understanding of the spatial distribution of building damage is essential for guiding rescue and resource allocation. In this study, we adopted the MambaBDA framework, which is built upon Mamba, a recently proposed state space architecture in the computer vision domain, and tailored it for spatio-temporal modeling of disaster impacts. The model was trained on the publicly available xBD dataset and subsequently applied to evaluate wildfire-induced building damage in Altadena, with pre- and post-disaster data acquired from WorldView-3 imagery captured during the 2025 Los Angeles wildfire. The workflow consisted of building localization and damage grading, followed by optimization to improve boundary accuracy and conversion to individual building-level assessments. Results show that about 28% of the buildings in Altadena suffered Major or Destroyed levels of damage. Population impact analysis, based on GHSL data, estimated approximately 3241 residents living in Major damage zones and 31,975 in Destroyed zones. These findings highlight the applicability of MambaBDA to wildfire scenarios, demonstrating its capability for efficient and transferable building damage assessment. The proposed approach provides timely information to support post-disaster rescue and recovery decision-making.