Content area
ABSTRACT
Advancements in technology and a reduction in its cost have led to substantial growth in the quality and 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 address this issue is to use pre-trained artificial intelligence models to process images onboard 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.
Details
Subject
Artificial intelligence;
Satellites;
Satellite observation;
Earth;
Constraints;
Image processing;
Satellite imagery;
Deep learning;
Computer science;
Data processing;
Landsat satellites;
Artificial satellites;
Machine learning;
Communication channels;
Remote sensing;
Edge computing;
Sensors;
Cost control;
Ground stations
Satellites;
Satellite observation;
Earth;
Constraints;
Image processing;
Satellite imagery;
Deep learning;
Computer science;
Data processing;
Landsat satellites;
Artificial satellites;
Machine learning;
Communication channels;
Remote sensing;
Edge computing;
Sensors;
Cost control;
Ground stations
Business indexing term
Identifier / keyword
Title
Advancing Earth observation: a survey on AI-powered image processing in satellites
Author
Duggan, Aidan 1 ; Andrade, Bruno 1 ; Afli, Haithem 1
1 Computer Science Department, Munster Technological University , Cork , Ireland
1 Computer Science Department, Munster Technological University , Cork , Ireland
European Journal of Remote Sensing; Cagiari
Volume
58
Issue
1
Number of pages
23
Publication year
2025
Publication date
Dec 1, 2025
Place of publication
Cagiari
Country of publication
United Kingdom
Publication subject
e-ISSN
22797254
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
Milestone dates
2025-02-06 (Received); 2025-09-22 (Accepted)
ProQuest document ID
3284639718
Document URL
https://www.proquest.com/scholarly-journals/advancing-earth-observation-survey-on-ai-powered/docview/3284639718/se-2?accountid=208611
Copyright
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2026-01-07
Database
ProQuest One Academic