Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning
There is growing interest toward the use of artificial intelligence (AI) directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This article presents a blueprint to the mission designer for the development of a modular and efficient deep learn...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10758783/ |
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| author | Gabriele Inzerillo Diego Valsesia Enrico Magli |
| author_facet | Gabriele Inzerillo Diego Valsesia Enrico Magli |
| author_sort | Gabriele Inzerillo |
| collection | DOAJ |
| description | There is growing interest toward the use of artificial intelligence (AI) directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This article presents a blueprint to the mission designer for the development of a modular and efficient deep learning payload to address multiple onboard inference tasks. In particular, we design a self-supervised lightweight backbone that provides features to efficient task-specific heads. The latter can be developed independently and with reduced data labeling requirements thanks to the frozen backbone. Experiments on three sample tasks of cloud segmentation, flood detection, and marine debris classification on a 7-W embedded system show competitive results with inference quality close to high-complexity state-of-the-art models and high throughput in excess of 8 Megapixel/s. |
| format | Article |
| id | doaj-art-5197dc4d3d2341dd830a2febcd4bdc3e |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-5197dc4d3d2341dd830a2febcd4bdc3e2025-08-20T01:56:13ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011882883810.1109/JSTARS.2024.350277610758783Efficient Onboard Multitask AI Architecture Based on Self-Supervised LearningGabriele Inzerillo0https://orcid.org/0009-0009-2027-0562Diego Valsesia1https://orcid.org/0000-0003-1997-2910Enrico Magli2https://orcid.org/0000-0002-0901-0251Department of Electronics and Telecommunications, Politecnico di Torino, Torino, ItalyDepartment of Electronics and Telecommunications, Politecnico di Torino, Torino, ItalyDepartment of Electronics and Telecommunications, Politecnico di Torino, Torino, ItalyThere is growing interest toward the use of artificial intelligence (AI) directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This article presents a blueprint to the mission designer for the development of a modular and efficient deep learning payload to address multiple onboard inference tasks. In particular, we design a self-supervised lightweight backbone that provides features to efficient task-specific heads. The latter can be developed independently and with reduced data labeling requirements thanks to the frozen backbone. Experiments on three sample tasks of cloud segmentation, flood detection, and marine debris classification on a 7-W embedded system show competitive results with inference quality close to high-complexity state-of-the-art models and high throughput in excess of 8 Megapixel/s.https://ieeexplore.ieee.org/document/10758783/Multitask learningonboard AIself-supervised learning (SSL) |
| spellingShingle | Gabriele Inzerillo Diego Valsesia Enrico Magli Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Multitask learning onboard AI self-supervised learning (SSL) |
| title | Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning |
| title_full | Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning |
| title_fullStr | Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning |
| title_full_unstemmed | Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning |
| title_short | Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning |
| title_sort | efficient onboard multitask ai architecture based on self supervised learning |
| topic | Multitask learning onboard AI self-supervised learning (SSL) |
| url | https://ieeexplore.ieee.org/document/10758783/ |
| work_keys_str_mv | AT gabrieleinzerillo efficientonboardmultitaskaiarchitecturebasedonselfsupervisedlearning AT diegovalsesia efficientonboardmultitaskaiarchitecturebasedonselfsupervisedlearning AT enricomagli efficientonboardmultitaskaiarchitecturebasedonselfsupervisedlearning |