Distributed Observation Allocation for a Large-Scale Constellation

Increased space and ground sensing are enabling new measurements of a wide range of Earth Science phenomena, including volcanism, flooding, wildfires, and weather. Large-scale observation constellations of hundreds of assets already exist (e.g. Planet) with several constellations of tens of thousands of assets planned. New challenges exist to rapidly assimilate available data and to optimize measurements (e.g. direct assets) to best observe these complex and dynamic spatiotemporal phenomena. We describe automated centralized and distributed Artificial Intelligence/Multi Agent methods to allocate observations in a constellation and compare their performance using realistic problem and orbit distributions.