利用航天器星座进行野火响应跟踪和增强预测的最优规划与机器学习
Optimal Planning and Machine Learning for Responsive Tracking and Enhanced Forecasting of Wildfires using a Spacecraft Constellation
AI总结：
针对野火监测，提出结合最优规划与机器学习的航天器星座操作概念，基于CYGNSS任务验证，提升数据收集效率、预测准确性与决策支持工具延迟性能，整合多源数据改进野火监测与预报。
摘要：
我们提出了一种新的操作概念，通过最优规划方法和机器学习（ML）来收集前所未有的星载野火监测数据，处理这些数据以生成野火危险或蔓延监测背景下的新产品或增强产品，并将其同化以改进现有的野火决策支持工具，这些工具可在适合时间关键型应用的延迟内交付给消防员。该概念针对NASA的CYGNSS任务进行了研究，CYGNSS是一个被动微波接收器星座，可在云层和烟雾环境下测量镜面GNSS-R反射。我们的规划器采用混合整数规划公式来安排所有卫星的联合观测数据收集和下行链路。快速找到的最优解决方案可收集98-100%的可用观测机会。驱动规划器目标的基于ML的火灾预测与地面实况的相关性比现有最先进技术高40%以上。所呈现的2024年德克萨斯州烟屋溪火灾和2025年洛杉矶火灾案例研究，是CYGNSS首次收集的活跃火灾高分辨率数据。利用ML对活跃火灾数据创建燃烧区域地图（BAM），以及使用神经网络将BAM同化到NASA的天气研究和预报模型中以广播火灾蔓延情况，均为新颖成果。BAM和CYGNSS获取的土壤湿度首次整合到USGS火灾危险地图中。将CYGNSS数据纳入基于ML的燃烧预测可将准确性提高13%，纳入高分辨率数据可将ML召回率再提高15%。所提出的工作流程预期延迟为6-30小时，相比当前数天的交付时间有所改进。所提出概念中的所有组件均显示出计算可扩展性和全球通用性，并考虑了边缘效率和小型设备低延迟等可持续性因素。
英文摘要：
We propose a novel concept of operations using optimal planning methods and machine learning (ML) to collect spaceborne data that is unprecedented for monitoring wildfires, process it to create new or enhanced products in the context of wildfire danger or spread monitoring, and assimilate them to improve existing, wildfire decision support tools delivered to firefighters within latency appropriate for time-critical applications. The concept is studied with respect to NASA's CYGNSS Mission, a constellation of passive microwave receivers that measure specular GNSS-R reflections despite clouds and smoke. Our planner uses a Mixed Integer Program formulation to schedule joint observation data collection and downlink for all satellites. Optimal solutions are found quickly that collect 98-100% of available observation opportunities. ML-based fire predictions that drive the planner objective are greater than 40% more correlated with ground truth than existing state-of-art. The presented case study on the TX Smokehouse Creek fire in 2024 and LA fires in 2025 represents the first high-resolution data collected by CYGNSS of active fires. Creation of Burnt Area Maps (BAM) using ML on data from active fires and BAM assimilation into NASA's Weather Research and Forecasting Model using neural nets to broadcast fire spread are novel outcomes. BAM and CYGNSS obtained soil moisture are integrated for the first time into USGS fire danger maps. Inclusion of CYGNSS data in ML-based burn predictions boosts accuracy by 13%, and inclusion of high-resolution data boosts ML recall by another 15%. The proposed workflow has an expected latency of 6-30h, improving on the current delivery time of multiple days. All components in the proposed concept are shown to be computationally scalable and globally generalizable, with sustainability considerations such as edge efficiency and low latency on small devices.
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。
