Design of a 2D Lattice Flower Constellation for Earth observation applying the twin satellite concept.

PandionAI is a Swedish start-up that aims to create a constellation of autonomous-tasking satellites for Earth observation and monitoring. Satellites in the constellation will carry built-in machine learning models that analyze the Earth images and detect early on environmental dangerous occurring events.
The aim of this thesis is to design a constellation for such purpose. A target list that includes places of interest for such events will be developed and the constellation performance will be evaluated with it by defining Key Performance Indicator parameters.
The target list has a dynamic nature as weather changes and climate evolves, so events of interest will be at different places in the globe as time passes. Because of this, four different target lists were developed, one per season. The events covered by the target lists are the environment endangering issues: fires, floods, deforestation, and CO2 footprint from factories.
Constellation design is a high complex problem due to the infinite possibilities of its formulation for a given purpose. Nevertheless, one can optimize for specific design parameters. This thesis proposes a model in which for a given number of total satellites, multiple constellations are compared, and the search for the optimal design is performed. Constellations in the optimization process are designed applying the 2D Lattice Flower Constellation method and adding the twin satellite concept.