Demonstrating Onboard Inference for Earth Science Applications with Spectral Analysis Algorithms and Deep Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909746527731712 |
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| author | Zilberstein, Itai Candela, Alberto Chien, Steve Rijlaarsdam, David Hendrix, Tom Buckley, Leonie Dunne, Aubrey |
| author_facet | Zilberstein, Itai Candela, Alberto Chien, Steve Rijlaarsdam, David Hendrix, Tom Buckley, Leonie Dunne, Aubrey |
| contents | In partnership with Ubotica Technologies, the Jet Propulsion Laboratory is demonstrating state-of-the-art data analysis onboard CogniSAT-6/HAMMER (CS-6). CS-6 is a satellite with a visible and near infrared range hyperspectral instrument and neural network acceleration hardware. Performing data analysis at the edge (e.g. onboard) can enable new Earth science measurements and responses. We will demonstrate data analysis and inference onboard CS-6 for numerous applications using deep learning and spectral analysis algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Demonstrating Onboard Inference for Earth Science Applications with Spectral Analysis Algorithms and Deep Learning Zilberstein, Itai Candela, Alberto Chien, Steve Rijlaarsdam, David Hendrix, Tom Buckley, Leonie Dunne, Aubrey Artificial Intelligence Machine Learning In partnership with Ubotica Technologies, the Jet Propulsion Laboratory is demonstrating state-of-the-art data analysis onboard CogniSAT-6/HAMMER (CS-6). CS-6 is a satellite with a visible and near infrared range hyperspectral instrument and neural network acceleration hardware. Performing data analysis at the edge (e.g. onboard) can enable new Earth science measurements and responses. We will demonstrate data analysis and inference onboard CS-6 for numerous applications using deep learning and spectral analysis algorithms. |
| title | Demonstrating Onboard Inference for Earth Science Applications with Spectral Analysis Algorithms and Deep Learning |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.15053 |