THU-Wildfire: A multitemporal, multimodal observation dataset for wildfire behavior dynamics

Abstract

High-quality observational data capturing the complete wildfire lifecycle are essential for validating and enhancing prediction models, yet such integrated datasets remain scarce. This study presents a modelling framework based on multitemporal data acquired through UAV sensing, including high-precision LiDAR, photogrammetry, and synchronous environmental monitoring. A multi-UAV relay observation strategy was developed to continuously record sub-second wildfire propagation dynamics. We demonstrate the utility of this framework through benchmark modelling experiments in fuel mapping, fire spread prediction, and burn severity assessment. The high-resolution data provide a valuable and comprehensive basis for evaluating model behavior across temporal scales, particularly in capturing early fire progression and fire-atmosphere interactions. It also reveals limitations in current modelling approaches. This work offers a robust resource for advancing wildfire environmental modelling.