Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites
Aidan Duggan, Bruno Andrade, Haithem Afli

TL;DR
This paper reviews recent AI-based methods for on-board image processing in Earth Observation satellites, addressing challenges and strategies to improve efficiency given satellite constraints.
Contribution
It provides a comprehensive survey of current research on AI-powered on-board satellite image processing, highlighting recent advancements and mitigation strategies.
Findings
Summarizes key constraints in satellite onboard processing
Details recent AI strategies to overcome these constraints
Highlights future research directions in the field
Abstract
Advancements in technology and reduction in it's cost have led to a substantial growth in the quality & quantity of imagery captured by Earth Observation (EO) satellites. This has presented a challenge to the efficacy of the traditional workflow of transmitting this imagery to Earth for processing. An approach to addressing this issue is to use pre-trained artificial intelligence models to process images on-board the satellite, but this is difficult given the constraints within a satellite's environment. This paper provides an up-to-date and thorough review of research related to image processing on-board Earth observation satellites. The significant constraints are detailed along with the latest strategies to mitigate them.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpacecraft Design and Technology
