{"id":56613,"date":"2026-01-19T10:06:32","date_gmt":"2026-01-19T09:06:32","guid":{"rendered":"https:\/\/www.nae.fr\/2026\/01\/19\/survey-on-ai-enabled-computer-vision-technologies-and-applications-for-space-robotic-missions\/"},"modified":"2026-01-19T10:06:32","modified_gmt":"2026-01-19T09:06:32","slug":"survey-on-ai-enabled-computer-vision-technologies-and-applications-for-space-robotic-missions","status":"publish","type":"post","link":"https:\/\/www.nae.fr\/en\/2026\/01\/19\/survey-on-ai-enabled-computer-vision-technologies-and-applications-for-space-robotic-missions\/","title":{"rendered":"Survey on AI-Enabled Computer Vision Technologies and Applications for Space Robotic Missions"},"content":{"rendered":"
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\n\nThis survey provides a comprehensive overview of recent advancements and challenges in Artificial Intelligence (AI)-enabled computer vision (CV) techniques for space robotic missions, spanning critical phases such as Entry, Descent, and Landing (EDL), orbital operations, and planetary surface exploration. Emphasis is placed on deep-learning\u2013based approaches for image classification, object detection, semantic segmentation, relative pose estimation, and feature matching. State-of-the-art methods in terrain-relative navigation, crater-based or rock-feature matching, and pose estimation for uncooperative targets are highlighted, illustrating the progress achieved through hybrid pipelines combining deep neural networks with classical geometry. The paper also critically evaluates publicly available orbital and planetary data sets\u2014along with the increasing role of synthetic data\u2014for developing and benchmarking CV algorithms under strict resource limitations and harsh environmental conditions.\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/blockquote>\n
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\n\nPour en savoir plus : Survey on AI-Enabled Computer Vision Technologies and Applications for Space Robotic Missions<\/a>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

This survey provides a comprehensive overview of recent advancements and challenges in Artificial Intelligence (AI)-enabled computer vision (CV) techniques for space robotic missions, spanning critical phases such as Entry, Descent, and Landing (EDL), orbital operations, and planetary surface exploration. Emphasis is placed on deep-learning\u2013based approaches for image classification, object detection, semantic segmentation, relative pose estimation, […]<\/p>\n","protected":false},"author":1,"featured_media":56614,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[24,16],"tags":[35],"class_list":["post-56613","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-actualite-spatial","category-rti","tag-actualites"],"acf":[],"yoast_head":"\nSurvey on AI-Enabled Computer Vision Technologies and Applications for Space Robotic Missions | Ton Site<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.nae.fr\/en\/2026\/01\/19\/survey-on-ai-enabled-computer-vision-technologies-and-applications-for-space-robotic-missions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Survey on AI-Enabled Computer Vision Technologies and Applications for Space Robotic Missions | Ton Site\" \/>\n<meta property=\"og:description\" content=\"This survey provides a comprehensive overview of recent advancements and challenges in Artificial Intelligence (AI)-enabled computer vision (CV) techniques for space robotic missions, spanning critical phases such as Entry, Descent, and Landing (EDL), orbital operations, and planetary surface exploration. Emphasis is placed on deep-learning\u2013based approaches for image classification, object detection, semantic segmentation, relative pose estimation, […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.nae.fr\/en\/2026\/01\/19\/survey-on-ai-enabled-computer-vision-technologies-and-applications-for-space-robotic-missions\/\" \/>\n<meta property=\"og:site_name\" content=\"NAE\" \/>\n<meta property=\"article:published_time\" content=\"2026-01-19T09:06:32+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.nae.fr\/wp-content\/uploads\/2026\/06\/Logo-Wiley-Online-Library.png\" \/>\n\t<meta property=\"og:image:width\" content=\"512\" \/>\n\t<meta property=\"og:image:height\" content=\"512\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"adminwa\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" 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