Enyi Jiang

The work proposes PyroAdapt, a pretrain–retrieve–rank framework that pretrains a wildfire risk model on historical data, retrieves similar historical locations using unlabeled target-year covariates, and fine-tunes through same-day fire–nonfire cell-pair ranking losses (direct ranking, residual pairwise DPO, and selective ranking); over 666 0.25° California grid cells, the three ranking objectives raise daily average precision from 21.62% under continued focal fine-tuning to 24.35–24.57% and Top5% recall from 18.70% to 22.11–22.79%, selective ranking captures 344 additional positive cell-days under a fixed daily budget of 34 cells (5% of area), and rolling evaluations over Yosemite show the ranking gains persist under temporal distribution shift.

The work proposes PyroAdapt, a pretrain–retrieve–rank framework that pretrains a wildfire risk model on historical data, retrieves similar historical locations using unlabeled target-year covariates, and fine-tunes through same-day fire–nonfire cell-pair ranking losses (direct ranking, residual pairwise DPO, and selective ranking); over 666 0.25° California grid cells, the three ranking objectives raise daily average precision from 21.62% under continued focal fine-tuning to 24.35–24.57% and Top5% recall from 18.70% to 22.11–22.79%, selective ranking captures 344 additional positive cell-days under a fixed daily budget of 34 cells (5% of area), and rolling evaluations over Yosemite show the ranking gains persist under temporal distribution shift.