用AI从卫星图自动识别建筑屋顶，帮加勒比小岛提升防灾能力
AI-Derived Structural Building Intelligence for Urban Resilience: An Application in Saint Vincent and the Grenadines
- 用地理空间大模型+浅层分类器从卫星图提取屋顶信息
- 屋顶坡度和材料识别F1得分分别达0.88和0.83
- 可为气候脆弱小岛国提供低成本防灾数据支持
详细建筑结构信息对评估飓风、洪水和滑坡等灾害潜在破坏至关重要，是城市韧性规划与减灾的基础。然而，在加勒比地区等气候脆弱的许多小岛屿发展中国家（SIDS）中，此类数据往往缺失。为此，本文提出一种AI驱动的工作流，通过高分辨率卫星影像自动推断屋顶属性，以圣文森特和格林纳丁斯为案例研究。我们比较了地理空间基础模型结合浅层分类器与微调深度学习模型在屋顶分类中的表现，并评估了引入邻近SIDS额外训练数据对模型性能的影响。最佳模型在屋顶坡度和屋顶材料分类上的F1得分分别为0.88和0.83。结合本地能力建设，本工作旨在帮助SIDS利用AI与地球观测（EO）数据，实现更高效、基于证据的城市治理。
原文摘要 · Abstract (English)
Detailed structural building information is used to estimate potential damage from hazard events like cyclones, floods, and landslides, making them critical for urban resilience planning and disaster risk reduction. However, such information is often unavailable in many small island developing states (SIDS) in climate-vulnerable regions like the Caribbean. To address this data gap, we present an AI-driven workflow to automatically infer rooftop attributes from high-resolution satellite imagery, with Saint Vincent and the Grenadines as our case study. Here, we compare the utility of geospatial foundation models combined with shallow classifiers against fine-tuned deep learning models for rooftop classification. Furthermore, we assess the impact of incorporating additional training data from neighboring SIDS to improve model performance. Our best models achieve F1 scores of 0.88 and 0.83 for roof pitch and roof material classification, respectively. Combined with local capacity building, our work aims to provide SIDS with novel capabilities to harness AI and Earth Observation (EO) data to enable more efficient, evidence-based urban governance.
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