标题： RoofNet：用于屋顶材料分类的全球多模态数据集 显示英文标题

标题： RoofNet: A Global Multimodal Dataset for Roof Material Classification

摘要： 自然灾害的频率和严重程度正在增加，每年造成数千亿美元的损失，并对基础设施和人类生计构成日益严重的威胁。 关于屋顶材料的准确数据对于建模建筑物对地震、洪水、野火和飓风等自然危害的脆弱性至关重要，但此类数据仍然无法获得。 为了解决这一问题，我们推出了RoofNet，这是迄今为止最大且地理上最多样化的新型多模态数据集，包含来自184个地理上多样的地点的超过51,500个样本，这些样本结合了高分辨率地球观测（EO）图像与全球屋顶材料分类的精心策划的文本注释。 RoofNet包括地理上多样的卫星图像，这些图像被标记为14种关键屋顶类型——例如沥青瓦、粘土瓦片和金属板——并设计用于通过视觉语言建模（VLM）增强全球暴露数据集的保真度。 我们从气候和建筑上截然不同的地区采样EO图块，构建代表性数据集。 与领域专家合作，对其中6,000张图像进行了注释，以微调VLM。 我们使用地理和材料感知提示调优来提高类别可分离性。 然后应用微调后的模型到剩余的EO图块上，通过基于规则和人机协作验证进行预测优化。 除了材料标签外，RoofNet还提供丰富的元数据，包括屋顶形状、占地面积、太阳能板存在情况以及混合屋顶材料（如暖通空调系统）的指示器。 RoofNet支持可扩展的AI驱动的风险评估，并作为下游基准，用于评估模型在各地区的泛化能力——为保险承保、灾害准备和基础设施政策规划提供切实可行的见解。 显示英文摘要

摘要： Natural disasters are increasing in frequency and severity, causing hundreds of billions of dollars in damage annually and posing growing threats to infrastructure and human livelihoods. Accurate data on roofing materials is critical for modeling building vulnerability to natural hazards such as earthquakes, floods, wildfires, and hurricanes, yet such data remain unavailable. To address this gap, we introduce RoofNet, the largest and most geographically diverse novel multimodal dataset to date, comprising over 51,500 samples from 184 geographically diverse sites pairing high-resolution Earth Observation (EO) imagery with curated text annotations for global roof material classification. RoofNet includes geographically diverse satellite imagery labeled with 14 key roofing types -- such as asphalt shingles, clay tiles, and metal sheets -- and is designed to enhance the fidelity of global exposure datasets through vision-language modeling (VLM). We sample EO tiles from climatically and architecturally distinct regions to construct a representative dataset. A subset of 6,000 images was annotated in collaboration with domain experts to fine-tune a VLM. We used geographic- and material-aware prompt tuning to enhance class separability. The fine-tuned model was then applied to the remaining EO tiles, with predictions refined through rule-based and human-in-the-loop verification. In addition to material labels, RoofNet provides rich metadata including roof shape, footprint area, solar panel presence, and indicators of mixed roofing materials (e.g., HVAC systems). RoofNet supports scalable, AI-driven risk assessment and serves as a downstream benchmark for evaluating model generalization across regions -- offering actionable insights for insurance underwriting, disaster preparedness, and infrastructure policy planning.