Reverse-Time SAM2 Mask Propagation for Scalable Labeling and Early Detection of Wildfire Smoke

Early wildfire detection from camera networks is limited less by detector architecture than by the quality of supervision. Human annotators often label faint, early-stage smoke plumes less reliably, causing models to perform poorly during this critical period. We address this bottleneck with an automated video-labeling pipeline based on SAM2 mask propagation performed in reverse temporal order, allowing masks anchored on clearly visible smoke to propagate backward to its earliest faint appearance. With targeted human validation to reject cloud-related artifacts, the pipeline produced a training dataset of 170,573 frames containing 128,185 smoke bounding boxes drawn from four data sources spanning fixed and pan-tilt-zoom camera networks as well as daytime and nighttime conditions. Using this dataset, we trained two complementary detectors: RT-DETR-L for high-recall early warning and YOLOv11x for high-precision confirmation. When evaluated on 144 HPWREN FIgLib ignition sequences, RT-DETR-L detected smoke in 111 of 144 sequences, with a mean detection time of 7.0 min after ignition, while YOLOv11x achieved high-precision confirmation with only four false-positive videos across the complete test set. The remaining detection failures were concentrated primarily under nighttime and foggy conditions, which we quantify and discuss in detail.