Laying the Foundation for AI-Based Tip and Cue
Enhancing Earth Observation Capabilities with Fewer Satellites
N.A. Duursma (TU Delft - Aerospace Engineering)
A. Menicucci – Mentor (TU Delft - Space Systems Egineering)
J.F.P. Kooij – Mentor (TU Delft - Intelligent Vehicles)
Gabriele Meoni – Mentor (European Space Agency (ESA))
Roberto Del Prete – Mentor (European Space Agency (ESA))
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Abstract
Wildfires spread in hours, ships change course in minutes, and natural disasters evolve faster than most satellites can observe. Current Earth Observation (EO) systems struggle to keep up: wide-area sensors provide coverage but little detail, while high-resolution satellites revisit too slowly.
This thesis, in collaboration with European Space Agency Φ-lab, investigates AI-based Tip and Cue, an EO strategy in which a wide field-of-view satellite (“Tip”) detects events and autonomously tasks a high-resolution satellite (“Cue”) for targeted imaging.
An end-to-end mission simulation framework was developed with three components: (1) an Orbital Simulation Framework modelling satellite dynamics, tasking, and pointing; (2) an Off-Nadir Imaging Rendering Pipeline generating off-nadir imagery with geometric and radiometric effects; and (3) an Onboard Detection Module deploying the transformer-based DEIMv2 model. A benchmark was developed and applied to whale detection.
Results show a two-satellite Tip-and-Cue system can approach the performance of a 16-satellite Walker constellation, achieving comparable detection rates, latency, and coverage with an order of magnitude fewer satellites.