Bharadwaj Chintalapati , Arthur Precht , Sougata Hanra , Rene Laufer , Marcus Liwicki , Jens Eickhoff
{"title":"Opportunities and challenges of on-board AI-based image recognition for small satellite Earth observation missions","authors":"Bharadwaj Chintalapati , Arthur Precht , Sougata Hanra , Rene Laufer , Marcus Liwicki , Jens Eickhoff","doi":"10.1016/j.asr.2024.03.053","DOIUrl":null,"url":null,"abstract":"<div><div>The satellite industry is rapidly growing. There has been a significant increase in the number of new small satellites that are launched, which is complemented by the rapid pace of the development of image recognition algorithms. Convolutional neural networks (CNNs) in particular, have achieved state-of-the-art performance in computer vision related applications. Combining both and running an AI algorithm on-board the satellite to observe and recognize any natural disaster directly from the orbit is an important opportunity. This paper presents notable challenges that are generally involved in an Earth Observation small satellite mission and further challenges that are posed by combining it with AI-based image recognition on-board the satellite. This study discusses an approach that is feasible mainly for a fleet of small satellites.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 9","pages":"Pages 6734-6751"},"PeriodicalIF":2.8000,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Space Research","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0273117724002886","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
The satellite industry is rapidly growing. There has been a significant increase in the number of new small satellites that are launched, which is complemented by the rapid pace of the development of image recognition algorithms. Convolutional neural networks (CNNs) in particular, have achieved state-of-the-art performance in computer vision related applications. Combining both and running an AI algorithm on-board the satellite to observe and recognize any natural disaster directly from the orbit is an important opportunity. This paper presents notable challenges that are generally involved in an Earth Observation small satellite mission and further challenges that are posed by combining it with AI-based image recognition on-board the satellite. This study discusses an approach that is feasible mainly for a fleet of small satellites.
期刊介绍:
The COSPAR publication Advances in Space Research (ASR) is an open journal covering all areas of space research including: space studies of the Earth''s surface, meteorology, climate, the Earth-Moon system, planets and small bodies of the solar system, upper atmospheres, ionospheres and magnetospheres of the Earth and planets including reference atmospheres, space plasmas in the solar system, astrophysics from space, materials sciences in space, fundamental physics in space, space debris, space weather, Earth observations of space phenomena, etc.
NB: Please note that manuscripts related to life sciences as related to space are no more accepted for submission to Advances in Space Research. Such manuscripts should now be submitted to the new COSPAR Journal Life Sciences in Space Research (LSSR).
All submissions are reviewed by two scientists in the field. COSPAR is an interdisciplinary scientific organization concerned with the progress of space research on an international scale. Operating under the rules of ICSU, COSPAR ignores political considerations and considers all questions solely from the scientific viewpoint.