OS-Mamba: Overall scanning Mamba for building damage assessment using multi-phase satellite imagery

Abstract

Building damage assessment (BDA) plays a crucial role in accelerating humanitarian relief efforts during natural disasters. Recent studies have shown that the state-space model-based Mamba architecture exhibits significant performance across various natural language processing tasks. In this paper, we propose a new model, OS-Mamba, which utilizes an Overall-Scan Convolution Modules (OSCM) for multidimensional global modeling of image backgrounds, enabling comprehensive capture and analysis of large spatial features from various directions, thereby enhancing the model's understanding and performance in complex scenes. Extensive experiments on the xBD dataset demonstrate that our proposed OS-Mamba model outperforms current state-of-the-art solutions.