Multi-Modal Building Change Detection for Large-Scale Small Changes: Benchmark and Baseline

• URL: http://arxiv.org/abs/2603.19077v1
• Date: Thu, 19 Mar 2026 16:05:05 GMT
• Title: Multi-Modal Building Change Detection for Large-Scale Small Changes: Benchmark and Baseline
• Abstract summary: Change detection in optical remote sensing imagery is susceptible to illumination fluctuations, seasonal changes, and variations in surface land-cover materials.<n>We introduce the Large-scale Small-change Multi-modal dataset (LSMD), a bi-temporal RGB-NIR building change detection benchmark dataset.<n>We further propose the Multi-modal Spectral Complementarity Network (MSCNet) to achieve effective cross-modal feature fusion.
• Score: 16.08630411584202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change detection in optical remote sensing imagery is susceptible to illumination fluctuations, seasonal changes, and variations in surface land-cover materials. Relying solely on RGB imagery often produces pseudo-changes and leads to semantic ambiguity in features. Incorporating near-infrared (NIR) information provides heterogeneous physical cues that are complementary to visible light, thereby enhancing the discriminability of building materials and tiny structures while improving detection accuracy. However, existing multi-modal datasets generally lack high-resolution and accurately registered bi-temporal imagery, and current methods often fail to fully exploit the inherent heterogeneity between these modalities. To address these issues, we introduce the Large-scale Small-change Multi-modal Dataset (LSMD), a bi-temporal RGB-NIR building change detection benchmark dataset targeting small changes in realistic scenarios, providing a rigorous testing platform for evaluating multi-modal change detection methods in complex environments. Based on LSMD, we further propose the Multi-modal Spectral Complementarity Network (MSCNet) to achieve effective cross-modal feature fusion. MSCNet comprises three key components: the Neighborhood Context Enhancement Module (NCEM) to strengthen local spatial details, the Cross-modal Alignment and Interaction Module (CAIM) to enable deep interaction between RGB and NIR features, and the Saliency-aware Multisource Refinement Module (SMRM) to progressively refine fused features. Extensive experiments demonstrate that MSCNet effectively leverages multi-modal information and consistently outperforms existing methods under multiple input configurations, validating its efficacy for fine-grained building change detection. The source code will be made publicly available at: https://github.com/AeroVILab-AHU/LSMD

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