FireSentry: 一种多模态时空基准数据集用于细粒度野火蔓延预测
FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting
AI总结：
FireSentry提出了一种多模态野火数据集和FiReDiff双模态范式，用于细粒度野火预测和动态灾害模拟，显著提升了视频和火斑掩膜的预测精度。
摘要：
细粒度野火蔓延预测对于提高应急响应效能和决策精确度至关重要。然而，现有研究大多集中在粗略的时空尺度上，并依赖低分辨率卫星数据，仅能捕捉宏观的火状态，从根本上限制了高精度局部火动态建模能力。为弥合这一差距，我们提出了FireSentry，一个省级规模的多模态野火数据集，其空间和时间分辨率分别达到亚米和亚秒级。通过同步无人机平台采集，FireSentry提供了可见光和红外视频流、现场环境测量数据以及人工验证的火斑掩膜。基于FireSentry，我们建立了涵盖物理建模、数据驱动和生成模型的综合性基准测试平台，揭示了现有仅依赖掩膜方法的局限性。我们的分析提出了FiReDiff，一种新的双模态范式，首先在红外模态中预测未来的视频序列，然后根据生成的动态精确分割火斑掩膜。FiReDiff在生成模型中实现了最先进的性能，视频质量在PSNR、SSIM、LPIPS、FVD上分别提高了39.2%、36.1%、50.0%、29.4%，火斑掩膜精度在AUPRC、F1分数、IoU、MSE上分别提高了3.3%、59.1%、42.9%、62.5%。FireSentry基准数据集和FiReDiff范式共同推动了细粒度野火预测和动态灾害模拟的发展。处理后的基准数据集已公开在：https://github.com/Munan222/FireSentry-Benchmark-Dataset。
英文摘要：
Fine-grained wildfire spread prediction is crucial for enhancing emergency response efficacy and decision-making precision. However, existing research predominantly focuses on coarse spatiotemporal scales and relies on low-resolution satellite data, capturing only macroscopic fire states while fundamentally constraining high-precision localized fire dynamics modeling capabilities. To bridge this gap, we present FireSentry, a provincial-scale multi-modal wildfire dataset characterized by sub-meter spatial and sub-second temporal resolution. Collected using synchronized UAV platforms, FireSentry provides visible and infrared video streams, in-situ environmental measurements, and manually validated fire masks. Building on FireSentry, we establish a comprehensive benchmark encompassing physics-based, data-driven, and generative models, revealing the limitations of existing mask-only approaches. Our analysis proposes FiReDiff, a novel dual-modality paradigm that first predicts future video sequences in the infrared modality, and then precisely segments fire masks in the mask modality based on the generated dynamics. FiReDiff achieves state-of-the-art performance, with video quality gains of 39.2% in PSNR, 36.1% in SSIM, 50.0% in LPIPS, 29.4% in FVD, and mask accuracy gains of 3.3% in AUPRC, 59.1% in F1 score, 42.9% in IoU, and 62.5% in MSE when applied to generative models. The FireSentry benchmark dataset and FiReDiff paradigm collectively advance fine-grained wildfire forecasting and dynamic disaster simulation. The processed benchmark dataset is publicly available at: https://github.com/Munan222/FireSentry-Benchmark-Dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
