RoofNet: A Global Multimodal Dataset for Roof Material Identification from Earth Observation

Abstract

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.