MDA-RSM: multi-directional adaptive remote sensing mamba for building extraction

Buildings are core carriers of urbanization and socio-economic development. Accurate extraction of buildings from remote sensing imagery is crucial for urban planning, population statistics, and economic assessment. However, existing convolutional neural network (CNN)-based methods struggle to effectively model the complex global contextual information of buildings, and vision Transformers (ViT) face computational constraints that limit their applicability to large-scale very high-resolution (VHR) imagery. To address these challenges, we introduce Mamba – a recently proposed state-space model with linear complexity – into the domain of building extraction. Motivated by the directional alignment and structural symmetry commonly observed in building layouts, we propose the Multi-Directional Adaptive Remote Sensing Mamba (MDA-RSM). Specifically, a Multi-Directional Scanning (MDS) module is designed to flexibly configure or extend Mamba's scanning directions based on task-specific requirements, thereby enhancing the model's adaptability to diverse remote sensing scenarios. On this basis, a Multi-Directional Attention Block (MDA) block is developed to dynamically model the contributions of different scanning directions, reinforce critical directional features, and suppress redundant or noisy information. Extensive experiments on the WHU, Inria, and Massachusetts building datasets demonstrate the effectiveness of the proposed MDA-RSM, achieving state-of-the-art performance under simple training strategies and validating the potential of directional modeling and the Mamba backbone in remote sensing tasks. The implementation will be available at https://github.com/openrsgis/MDA-RSM.