HuggingFace Daily Papers（社区热门论文）· HuggingFace Daily Papers（社区热门论文）·· 8 天前AI 评分37
PyroAdapt：面向空间异质性与时间漂移的山火预测自适应框架
AI 导读
PyroAdapt 用"预训练—检索—排序"框架让山火预测模型适应目标分布：在加州 666 个 0.25x0.25 网格上，选择性排序将日均精度从 21.62% 提升至 24.35–24.57%，Top5% 召回率从 18.70% 升至 22.11–22.79%。
正文
Abstract:Prediction of wildfire occurrence is a rare-event problem compounded by spatial heterogeneity and temporal distribution shift, as fire occurrences are vastly outnumbered by non-occurrences, and predictor--fire relationship varies across space and time. Models trained on historical fire data may perform poorly under new conditions and require adaptation to the target distribution before operational use. We propose PyroAdapt, a pretrain--retrieve--rank framework that adapts a pretrained model to target conditions by retrieving historical locations with similar conditions and fine-tuning on the retrievals through risk ranking. For spatial adaptation, we condition risk on terrain, ecoregion embeddings, and fire rates, accounting for spatial context in the retrieval, and learn risk ordering from same-day fire--nonfire cell pairs. We compare direct ranking, residual pairwise DPO (RDPO), and selective ranking through a unified score-gap formulation that characterizes their gradient allocation. Over California (discretized into 666 0.25x0.25 grid cells), these objectives raise daily average precision from 21.62% for continued focal fine-tuning to 24.35--24.57%, and Top5% recall from 18.70% to 22.11--22.79%. Under a fixed daily detection budget of 34 cells (5% area), selective ranking captures 344 additional positive cell--days. For fires in the top 5%/10%/20% of dry matter consumption, selective ranking raises recall by 39.70/28.18/20.50 percentage points, respectively. Furthermore, rolling evaluations over Yosemite show that the gains from ranking persist under temporal distribution shift. Together, these results show that PyroAdapt prioritizes the most fire-prone locations under a daily budget constraint and detects more extreme fire events.
| Subjects: | Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2605.12435 [cs.LG] |
| (or arXiv:2605.12435v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12435 arXiv-issued DOI via DataCite |
Submission history
From: Enyi Jiang [view email]
[v1]
Tue, 12 May 2026 17:31:00 UTC (230 KB)
[v2]
Mon, 28 Sep 2026 04:29:15 UTC (166 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org