Building-level exposure data are critical to natural hazard risk modeling, yet most global inventories describe where buildings are located rather than what they are made of. Roof material is a critical but poorly documented attribute for assessing vulnerability to wildfires, wind hazards, urban heat, floods, and earthquakes. To address this gap, we introduce RoofNet, a global dataset that maps 49,662 georeferenced building instances from 101 countries to 14 key roofing material classes using Earth observation (EO) imagery (redistributed where permitted) and associated geospatial metadata. RoofNet contributes (1) climatographically and architecturally diverse coverage of roof material labels, (2) a scalable annotation pipeline combining SME-guided manual labeling with vision-language model (VLM)-assisted classification, rule-based validation, and human-in-the-loop verification, and (3) a resource for evaluating subtle, geographically variable material-level identification in EO imagery and its implications for material-aware hazard risk modeling. Evaluation on a manually labeled hold-out set shows that zero-shot Remote Contrastive Language-Image Pre-Training (RemoteCLIP) struggles with roof material classification, while fine-tuning with RoofNet improves top-1 accuracy from 4.9% to 47.7%. We use RoofNet in an illustrative hazard case study to demonstrate how material-aware exposure data can change vulnerability estimates relative to material-naive inventories. RoofNet provides a missing material layer for global building attribute mapping and scalable hazard risk assessment.

翻译：建筑级暴露数据对自然灾害风险建模至关重要，但现有全球清单大多描述建筑位置而非建筑材料。屋顶材料是评估火灾、风灾、城市热浪、洪水和地震脆弱性的关键属性，但目前相关记录严重不足。为弥补这一缺口，我们提出RoofNet——一个涵盖101个国家49,662个地理参考建筑实例的全球数据集，通过地球观测影像（在许可范围内重新分发）与相关地理空间元数据，将建筑映射至14种关键屋顶材料类别。RoofNet的贡献包括：(1) 提供气候地理与建筑学多样性兼具的屋顶材料标签覆盖；(2) 构建可扩展的标注管线，融合领域专家指导的手动标注、视觉-语言模型辅助分类、基于规则的验证及人在环路的核查机制；(3) 为评估地球观测影像中地理变异显著的细微材料级识别及其对材料感知型灾害风险建模的影响提供资源。在手动标注的留置集上评估显示，零样本遥感对比语言-图像预训练模型在屋顶材料分类中表现欠佳，而经RoofNet微调后，top-1准确率从4.9%提升至47.7%。通过示例灾害案例研究，我们展示了材料感知型暴露数据如何改变材料朴素型清单的脆弱性评估结果。RoofNet为全球建筑属性映射与可扩展灾害风险评估提供了缺失的材料层信息。