FireScope：以思维链预言机进行野火风险栅格预测
FireScope: Wildfire Risk Raster Prediction With a Chain-of-Thought Oracle
把 VLM 的思维链推理作为生成式空间预测的中间监督，用强化学习加视觉监督联合训练，让语言推理轨迹提升栅格生成的跨大陆泛化——这套「推理到生成」的范式可迁移到其他需要多源遥感输入与连续场输出的视觉任务。
原文依据 a VLM-based reasoning-to-generation framework that learns from both reinforcement learning and visual supervision
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论野火风险预测是一个推理密集型的空间问题，需要整合视觉、气候与地理因素来推断连续的风险图。现有方法缺乏可靠泛化所需的因果推理与多模态理解能力。我们提出 FireScope-Bench，这是一个大规模数据集与基准，将 Sentinel-2 影像和气候数据与美国范围内专家定义的风险栅格相耦合，并纳入欧洲的真实野火事件以支持跨大陆评测。基于该数据集，我们提出 FireScope，一个基于 VLM 的「推理到生成」框架，同时从强化学习与视觉监督中学习，以互补的推理轨迹预测风险栅格。在美国训练、在欧洲测试时，FireScope 取得了显著的性能提升，同时专家反馈与自动化分析确认其推理轨迹是忠实的且具有语义意义。我们的发现表明，推理能够为栅格预测模型提供依据，从而同时提升泛化性与可解释性。据我们所知，这是首个（1）证明基于语言的推理能够提升视觉生成泛化性的框架，（2）提出可跨大陆应用的高分辨率野火风险模型，以及（3）支持对多模态火灾风险模型的稳健跨大陆泛化进行系统性研究的框架。我们相信 FireScope-Bench 有潜力成为推进推理驱动、可解释且可泛化的空间建模的基础。数据与源代码见 github.com/insait-institute/FireScope。
英文原文摘要 1659 字符
Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer continuous risk maps. Existing methods lack the causal reasoning and multimodal understanding required for reliable generalization. We introduce FireScope-Bench, a large-scale dataset and benchmark that couples Sentinel-2 imagery and climate data with expert-defined risk rasters across the USA, and real wildfire events in Europe for cross-continental evaluation. Building on this dataset, we propose FireScope, a VLM-based reasoning-to-generation framework that learns from both reinforcement learning and visual supervision to predict risk rasters with complementary reasoning traces. When trained in the USA and tested in Europe, FireScope achieves substantial performance gains, while expert feedback and automated analysis confirm that its reasoning traces are faithful and semantically meaningful. Our findings demonstrate that reasoning can ground raster prediction models, improving both generalization and interpretability. To our knowledge, this is the first framework to (1) demonstrate that language-based reasoning can improve generalization in visual generation, (2) propose a high-resolution wildfire risk model that can be applied across continents, and (3) enable systematic studies of robust cross-continental generalization for multimodal fire risk models. We believe that FireScope-Bench has the potential to serve as a foundation for advancing reasoning-driven, interpretable and generalizable spatial modeling. Data and source code are available at github.com/insait-institute/FireScope.