Automating the Wildfire Detection and Scheduling Pipeline with Maneuverable Earth Observation Satellites

Wildfires are becoming increasingly frequent, with potentially devastating consequences, including loss of life, infrastructure destruction, and severe environmental damage. Low Earth orbit satellites equipped with onboard sensors can capture critical imagery of active wildfires and enable real-time detection through machine learning algorithms applied to the acquired data. This paper presents a framework that automates the complete wildfire detection and scheduling pipeline, integrating three key components: wildfire detection in satellite imagery, statistical updating that incorporates data from repeated flyovers, and multi-satellite scheduling optimization. The framework enables wildfire detection using convolutional neural networks with sensor fusion techniques, the incorporation of subsequent flyover information using Bayesian statistics, and satellite scheduling through the state-of-the-art Reconfigurable Earth Observation Satellite Scheduling Problem. Experiments conducted using real-world wildfire events and operational Earth observation satellites demonstrate that this autonomous detection and scheduling approach effectively enhances wildfire monitoring capabilities.