FireResBench: An Event-Centered Benchmark for Wildfire Lifecycle Understanding and Operational-Response Forecasting

Wildfire emergency management requires responders to continuously assess evolving fire behavior, incident status, containment progress, and resource deployment using scattered and irregular operational records. Existing wildfire datasets have largely been designed for physical fire detection, burned-area mapping, or spread forecasting, leaving evolving incident observations disconnected from real-world response histories. To bridge this gap, we introduce **FireResBench**, an event-centered benchmark that connects wildfire progression with emergency-response records by organizing operational records into temporally ordered Fire-Day histories. FireResBench supports two complementary tasks: (1) wildfire lifecycle-state understanding and (2) daily personnel and cost forecasting. To provide fire lifecycle supervision, a two-stage labeling paradigm is adopted. In the first stage, carefully designed labeling rules guide an auto-labeling code pipeline that generates provisional labels. In the second stage, domain scholars verify and correct the labels to obtain the final annotations. The released dataset covers the conterminous United States from January 2017 to December 2020 and contains 33,303 Fire-Day instances from 5,728 incidents. It provides scholar-verified lifecycle labels, temporally valid daily personnel and cost targets, incident-disjoint splits, and carefully designed evaluation protocols. Experiments across representative machine learning, tabular, and deep learning paradigms reveal that accurate forecasting on lifecycle transitions and high-demand Fire-Days remain particularly challenging. By converting fragmented public records into auditable event sequences and reproducible learning tasks, FireResBench establishes a foundation for data-driven wildfire-response modeling. The benchmark and evaluation resources are available at: https://anonymous.4open.science/r/FireResBench-0A9D/.