Smartphone-based mapping of home ignition zone attributes for wildland–urban interface fire risk assessment

Background Assessing home ignition zone attributes is essential for reducing wildfire risk in wildland–urban interface (WUI) communities; however, traditional satellite imagery often lacks sufficient spatial resolution and timely updates to capture localized fuel conditions near structures. Aims This study develops and validates a smartphone-based framework for real-time detection, mapping and 3D visualization of building features and defensible space fuel to support wildfire risk assessment and mitigation in WUI environments. Methods The smartphone-based framework was developed by training multiple YOLO models using 4413 annotated images representing 38 home ignition zone attributes. The best-performing model was integrated with multi-object tracking, monocular depth estimation and smartphone sensor data to geolocate detections and generate 3D maps from real-world videos. Key results The proposed framework successfully detected and mapped home ignition zone attributes. YOLOv11 achieved the best performance with near-real time processing capability. The integrated framework enabled approximate geospatial localization and visualization of home ignition zone attributes, capturing fine-scale fuel conditions and recent environmental changes unavailable in satellite imagery. Conclusions and implications The proposed framework provides a low-cost, scalable approach for mapping home ignition zone attributes and supports real-time fuel characterization, defensible space assessment, wildfire risk analysis, mitigation planning, emergency response and community preparedness in WUI communities.