A review of vision-based remote sensing datasets for monitoring of Canadian wildland-urban interfaces

The Wildland-Urban Interface (WUI) marks the zone where urban development meets natural ecosystems, creating heightened wildfire risks, environmental challenges, and public safety concerns. Addressing these risks requires enhanced WUI monitoring supported by comprehensive data collection for mitigation strategies, early detection, suppression, and evacuation planning. This study reviews Canadian datasets relevant to wildfire risk assessment, evaluating their availability, quality, and suitability through a dual-track approach considering both supply and demand perspectives. Criteria include spatial and temporal resolution, accessibility, and thematic relevance to hazard assessment, detection, and infrastructure protection. The gap analysis shows that existing datasets strongly support active fire detection and burned-area characterization, but remain insufficient for WUI-specific planning variables. While current datasets are useful for active fire detection, they lack detail on canopy structure, defensible space, fuel proximity, and detailed infrastructure mapping, while high-resolution datasets like FLAME 3 and CLOUD offer potential but suffer from limited coverage and real-time capability. Based on these findings, we recommend prioritizing standardized, high-resolution, regularly updated WUI datasets and enabling their integration with UAV-based thermal and LiDAR observations to address the most critical missing layers. The study recommends standardized, high-resolution, regularly updated datasets integrated with UAV-based thermal and LiDAR technologies to strengthen WUI wildfire management.