Optimal Planning and Machine Learning for Responsive Tracking and Enhanced Forecasting of Wildfires using a Spacecraft Constellation
🤖 AI Summary
To address the challenges of delayed wildfire monitoring response and insufficient prediction accuracy, this paper proposes an end-to-end wildfire response framework integrating spacecraft constellation scheduling optimization with machine learning. Methodologically, it introduces high-resolution soil moisture and burn-scar data from the CYGNSS mission into wildfire hazard modeling for the first time; designs a joint satellite observation-and-downlink scheduling strategy based on mixed-integer programming; and develops a multi-task machine learning model enabling dynamic burn-scar mapping, fire spread forecasting, and multi-source data fusion. Experimental results demonstrate that the system completes the full response pipeline within 6–30 hours, achieves an observation opportunity capture rate ≥98%, and improves burn-area prediction accuracy and recall by 13% and 15%, respectively—significantly outperforming existing decision-support tools.
Application Category
📝 Abstract
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 applied to the data during active fires and BAM assimilation into NASA's Weather Research and Forecasting Model using ML 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.
Problem
Research questions and friction points this paper is trying to address.
Optimize wildfire monitoring using spacecraft data and ML
Enhance fire spread forecasts with real-time satellite observations
Improve decision tools for firefighters with low-latency data processing
Innovation
Methods, ideas, or system contributions that make the work stand out.
Optimal planning for wildfire data collection
Machine learning enhances fire prediction accuracy
Integration of CYGNSS data improves fire monitoring
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