自然灾害的发生频率与强度持续上升，每年造成数千亿美元损失，并对基础设施与人类生计构成日益严峻的威胁。准确的屋顶材料数据对于建模建筑物在地震、洪水、野火和飓风等自然灾害下的脆弱性至关重要，但此类数据目前仍不可得。为填补这一空白，我们提出 RoofNet——迄今规模最大、地理覆盖最广的新型多模态数据集，包含来自184个地理分布广泛地点的逾51,500个样本，将高分辨率地球观测（EO）影像与人工整理的文本标注配对，用于全球屋顶材料分类。RoofNet 包含来自气候与建筑特征各异区域的地理多样性卫星影像，标注了14类关键屋顶材料，旨在通过视觉-语言建模（VLM）提升全球暴露度数据集的精度。我们从气候与建筑特征显著不同的区域采样 EO 影像瓦片，构建具有代表性的数据集。其中6,000张图像由领域专家协作完成标注，用于微调 VLM 模型；采用兼顾地理位置与材料特性的提示调优（prompt tuning）以增强类别可分性。微调后的模型被应用于其余 EO 瓦片，其预测结果经基于规则的方法与人工参与（human-in-the-loop）验证进一步优化。除材料标签外，RoofNet 还提供丰富的元数据，包括屋顶形状、轮廓面积、太阳能板存在性，以及混合屋顶材料（如暖通空调系统）的指示信息。因影像来源存在许可限制，早期实验所用数据集已被移除。基于该数据集所得结果应谨慎解读；使用合规数据的更新实验正在进行中。

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 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). The dataset used in earlier experiments has been removed due to licensing constraints related to imagery sources. Results based on this dataset should be interpreted with caution. Updated experiments using compliant data are in progress.