WildfireIA: Can Environmental Data at Fire Discovery Time Predict Whether a Wildfire Will Escape Initial Attack?

Initial attack (IA) refers to the first suppression actions taken to contain or extinguish a wildfire following its detection. Existing IA failure prediction studies often use non-public response records or regional settings, so it remains unclear how well public data aligned to the reported discovery day can support IA failure prediction at national scale. We present WildfireIA, a novel U.S. national-scale benchmark for IA failure prediction under a discovery-day information contract using public environmental and contextual data. WildfireIA aligns 38,128 naturally caused FPA-FOD wildfire events with FIRMS/VIIRS thermal detections, gridMET weather and fire-danger variables, LANDFIRE vegetation, fuel, and topography, OpenStreetMap access features, and WorldPop population density. To prevent data leakage, the benchmark fixes the event unit, size-based label rule, chronological split, metrics, and forbidden-feature list, and excludes final fire size, containment timestamps, and D+1 and later satellite detections from model inputs. We evaluate 16 representative models across tabular, temporal, spatial, and spatiotemporal families under the same protocol. Results show that public discovery-day data provides useful but incomplete signal for IA failure prediction: XGBoost achieves the best AUPRC of 53.3%; FIRMS/VIIRS is the least redundant source; and fuel is the strongest static predictor when dynamic observations are unavailable. We release preprocessing outputs and model-ready caches to support reproducible research on early wildfire risk assessment: https://github.com/WildfireIA-anonymous/WildfireIA.