Francesca Razzano;Pietro Di Stasio;Francesco Mauro;Gabriele Meoni;Marco Esposito;Gilda Schirinzi;Silvia Liberata Ullo
{"title":"在未来的 $\\Phi$sat-2 飞行任务上对沿海水域污染物进行近实时监测的人工智能技术","authors":"Francesca Razzano;Pietro Di Stasio;Francesco Mauro;Gabriele Meoni;Marco Esposito;Gilda Schirinzi;Silvia Liberata Ullo","doi":"10.1109/JSTARS.2024.3455992","DOIUrl":null,"url":null,"abstract":"Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing data, Artificial Intelligence techniques, and onboard processing. While conventional procedures present several drawbacks mainly related to late intervention capabilities, the objective of what proposed is to offer nearly real-time detection of contaminants in coastal waters addressing a significant gap in the existing literature and allowing fast alerts and intervention. In fact, the expected outcomes include substantial advancements in environmental monitoring, public health protection, and resource conservation. Namely, 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 OrbitalAI Challenge, this article describes the distinctive opportunities and issues for the contaminants' monitoring on the \n<inline-formula><tex-math>$\\Phi$</tex-math></inline-formula>\nsat-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 presented, along with an introduction to ongoing and future work.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670116","citationCount":"0","resultStr":"{\"title\":\"AI Techniques for Near Real-Time Monitoring of Contaminants in Coastal Waters on Board Future $\\\\Phi$sat-2 Mission\",\"authors\":\"Francesca Razzano;Pietro Di Stasio;Francesco Mauro;Gabriele Meoni;Marco Esposito;Gilda Schirinzi;Silvia Liberata Ullo\",\"doi\":\"10.1109/JSTARS.2024.3455992\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing data, Artificial Intelligence techniques, and onboard processing. While conventional procedures present several drawbacks mainly related to late intervention capabilities, the objective of what proposed is to offer nearly real-time detection of contaminants in coastal waters addressing a significant gap in the existing literature and allowing fast alerts and intervention. In fact, the expected outcomes include substantial advancements in environmental monitoring, public health protection, and resource conservation. Namely, 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 OrbitalAI Challenge, this article describes the distinctive opportunities and issues for the contaminants' monitoring on the \\n<inline-formula><tex-math>$\\\\Phi$</tex-math></inline-formula>\\nsat-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. 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AI Techniques for Near Real-Time Monitoring of Contaminants in Coastal Waters on Board Future $\Phi$sat-2 Mission
Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing data, Artificial Intelligence techniques, and onboard processing. While conventional procedures present several drawbacks mainly related to late intervention capabilities, the objective of what proposed is to offer nearly real-time detection of contaminants in coastal waters addressing a significant gap in the existing literature and allowing fast alerts and intervention. In fact, the expected outcomes include substantial advancements in environmental monitoring, public health protection, and resource conservation. Namely, 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 OrbitalAI Challenge, this article describes the distinctive opportunities and issues for the contaminants' monitoring on the
$\Phi$
sat-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 presented, along with an introduction to ongoing and future work.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.