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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2404.19586 |
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| _version_ | 1866914777744277504 |
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| author | Razzano, Francesca Di Stasio, Pietro Mauro, Francesco Meoni, Gabriele Esposito, Marco Schirinzi, Gilda Ullo, Silvia L. |
| author_facet | Razzano, Francesca Di Stasio, Pietro Mauro, Francesco Meoni, Gabriele Esposito, Marco Schirinzi, Gilda Ullo, Silvia L. |
| contents | Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing (RS) data, Artificial Intelligence (AI) techniques, and onboard processing. The objective is to offer nearly real-time detection of contaminants in coastal waters addressing a significant gap in the existing literature. Moreover, the expected outcomes include substantial advancements in environmental monitoring, public health protection, and resource conservation. The specific focus of our study is on the estimation of Turbidity and pH parameters, for their implications on human and aquatic health. Nevertheless, the designed framework can be extended to include other parameters of interest in the water environment and beyond. Originating from our participation in the European Space Agency (ESA) OrbitalAI Challenge, this article describes the distinctive opportunities and issues for the contaminants monitoring on the Phisat-2 mission. The specific characteristics of this mission, with the tools made available, will be presented, with the methodology proposed by the authors for the onboard monitoring of water contaminants in near real-time. Preliminary promising results are discussed and in progress and future work introduced. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19586 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 mission Razzano, Francesca Di Stasio, Pietro Mauro, Francesco Meoni, Gabriele Esposito, Marco Schirinzi, Gilda Ullo, Silvia L. Computer Vision and Pattern Recognition Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing (RS) data, Artificial Intelligence (AI) techniques, and onboard processing. The objective is to offer nearly real-time detection of contaminants in coastal waters addressing a significant gap in the existing literature. Moreover, the expected outcomes include substantial advancements in environmental monitoring, public health protection, and resource conservation. The specific focus of our study is on the estimation of Turbidity and pH parameters, for their implications on human and aquatic health. Nevertheless, the designed framework can be extended to include other parameters of interest in the water environment and beyond. Originating from our participation in the European Space Agency (ESA) OrbitalAI Challenge, this article describes the distinctive opportunities and issues for the contaminants monitoring on the Phisat-2 mission. The specific characteristics of this mission, with the tools made available, will be presented, with the methodology proposed by the authors for the onboard monitoring of water contaminants in near real-time. Preliminary promising results are discussed and in progress and future work introduced. |
| title | AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 mission |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.19586 |