Schedule Earth Observation satellites with Deep Reinforcement Learning
This addresses the challenge of reducing wasted satellite images due to cloud cover for satellite operators and customers, but it is incremental as it builds on existing DRL methods in a specific domain.
The paper tackles the problem of scheduling Earth observation satellites to acquire cloud-free images by proposing a Deep Reinforcement Learning approach, finding that it challenges classical human-expert heuristic methods in a simplified environment.
Optical Earth observation satellites acquire images worldwide , covering up to several million square kilometers every day. The complexity of scheduling acquisitions for such systems increases exponentially when considering the interoperabil-ity of several satellite constellations together with the uncertainties from weather forecasts. In order to deliver valid images to customers as fast as possible, it is crucial to acquire cloud-free images. Depending on weather forecasts, up to 50% of images acquired by operational satellites can be trashed due to excessive cloud covers, showing there is room for improvement. We propose an acquisition scheduling approach based on Deep Reinforcement Learning and experiment on a simplified environment. We find that it challenges classical methods relying on human-expert heuristic.