2026
Journal article
Open Access
Research progress on Ocean observations technology and information systems
Tsabaris Christos, Pieri Gabriele
The oceans play a crucial role in the global ecosystem; they shape trends in the climate, weather, water management, and health (including biogeochemical cycles). Although innovative technologies have been developed for the marine environment to better understand the ocean’s processes, several constraints remain in ocean observation, hindering the replacement of laboratory and routine monitoring methods. TSource: JOURNAL OF MARINE SCIENCE AND ENGINEERING, vol. 14 (issue 9)
DOI: 10.3390/jmse14090774
Project(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
Metrics:
Tsabaris Christos, Pieri Gabriele
The oceans play a crucial role in the global ecosystem; they shape trends in the climate, weather, water management, and health (including biogeochemical cycles). Although innovative technologies have been developed for the marine environment to better understand the ocean’s processes, several constraints remain in ocean observation, hindering the replacement of laboratory and routine monitoring methods. TSource: JOURNAL OF MARINE SCIENCE AND ENGINEERING, vol. 14 (issue 9)
DOI: 10.3390/jmse14090774
Project(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
Metrics:
See at:
Journal of Marine Science and Engineering 
| IRIS Cnr | IRIS Cnr | CNR IRIS 
2025
Other
Open Access
SI-Lab Annual Research Report 2024
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
2025
Other
Restricted
NAUTILOS - New approach to underwater technologies for innovative, low-cost ocean observation
Pieri G.
Presentazione del progetto NAUTILOS coordinato dal CNR e con la partecipazione congiunta di ISTI e IRBIMProject(s): NAUTILOS
Pieri G.
Presentazione del progetto NAUTILOS coordinato dal CNR e con la partecipazione congiunta di ISTI e IRBIMProject(s): NAUTILOS
2025
Conference article
Open Access
Deep learning–based detection of Nephrops norvegicus burrows
Papini O., Cecapolli E., Domenichetti F., Pieri G., Reggiannini M., Zacchetti L., Martinelli M.
This work presents a methodology to support the assessment of a benthic species of great commercial importance (Nephrops norvegicus) taking advantage of a combination of machine learning and computer vision methods. Up to the present, abundance indices based on the density of this species are evaluated by visual inspection of underwater imagery and through manual counting of the observed burrows. A novel approach is proposed, based on the integration in the processing pipeline of a supervised learning model in charge of detecting the burrows. The model is trained exploiting underwater videos that experts annotate by identifying the frames where burrows are present and specifying the related features. To pursue such a goal, the proposed automated procedure must cope with several environmental issues, such as high underwater turbidity, uneven illumination, heavy colour distortions, as well as complexities arising from the presence of ambiguous objects and morphological features that may affect the misclassification rate. The proposed method was developed on video material collected in a specific area, but has the potential to be applied throughout the species' distribution range. Preliminary results concerning the analysis of data captured in the central Adriatic Sea are presented and discussed.DOI: 10.1109/metrosea66681.2025.11245690
Project(s): NAUTILOS
Metrics:
Papini O., Cecapolli E., Domenichetti F., Pieri G., Reggiannini M., Zacchetti L., Martinelli M.
This work presents a methodology to support the assessment of a benthic species of great commercial importance (Nephrops norvegicus) taking advantage of a combination of machine learning and computer vision methods. Up to the present, abundance indices based on the density of this species are evaluated by visual inspection of underwater imagery and through manual counting of the observed burrows. A novel approach is proposed, based on the integration in the processing pipeline of a supervised learning model in charge of detecting the burrows. The model is trained exploiting underwater videos that experts annotate by identifying the frames where burrows are present and specifying the related features. To pursue such a goal, the proposed automated procedure must cope with several environmental issues, such as high underwater turbidity, uneven illumination, heavy colour distortions, as well as complexities arising from the presence of ambiguous objects and morphological features that may affect the misclassification rate. The proposed method was developed on video material collected in a specific area, but has the potential to be applied throughout the species' distribution range. Preliminary results concerning the analysis of data captured in the central Adriatic Sea are presented and discussed.DOI: 10.1109/metrosea66681.2025.11245690
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | CNR IRIS
2025
Other
Open Access
NAUTILOS D9.7 KPI Assessment 2
Dapueto G., Novellino A., Pieri G., Martinelli M., Ntoumas M., Chatzinikolaou E., Lemos C, Bastos Da Cruz Martins F. A., Sà S, Marty S., Smerdon A., Alonso I., Dimitrova L., Seppälä J., Malardé D., Alba M., Viglino P., Oliveri A.
The Deliverable assess the Key Performance Indicators (KPIs) for the NAUTILOS data management infrastructureDOI: 10.5281/zenodo.18245176
Project(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
Metrics:
Dapueto G., Novellino A., Pieri G., Martinelli M., Ntoumas M., Chatzinikolaou E., Lemos C, Bastos Da Cruz Martins F. A., Sà S, Marty S., Smerdon A., Alonso I., Dimitrova L., Seppälä J., Malardé D., Alba M., Viglino P., Oliveri A.
The Deliverable assess the Key Performance Indicators (KPIs) for the NAUTILOS data management infrastructureDOI: 10.5281/zenodo.18245176
Project(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
Metrics:
2025
Conference article
Open Access
Machine learning approaches for automated detection of nephrops norvegicus burrows in underwater surveys
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
This paper presents an analysis of computer vision methods designed to automate the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. The proposed approach seeks to evaluate the accuracy, minimise human error, and standardise the existing manual video analysis process. By leveraging machine learning techniques, the system described in this paper autonomously processes video streams and identifies N. norvegicus burrow openings on the seabed. Additionally, this study investigates data augmentation algorithms to expand an annotated dataset and evaluates the performances of the first results under different configurations.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15616, pp. 285-294. Kolkata, India, 01-05/12/2024
DOI: 10.1007/978-3-031-87663-9_24
Project(s): NAUTILOS
Metrics:
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
This paper presents an analysis of computer vision methods designed to automate the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. The proposed approach seeks to evaluate the accuracy, minimise human error, and standardise the existing manual video analysis process. By leveraging machine learning techniques, the system described in this paper autonomously processes video streams and identifies N. norvegicus burrow openings on the seabed. Additionally, this study investigates data augmentation algorithms to expand an annotated dataset and evaluates the performances of the first results under different configurations.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15616, pp. 285-294. Kolkata, India, 01-05/12/2024
DOI: 10.1007/978-3-031-87663-9_24
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Machine learning for the evaluation of the Nephrops norvegicus Population
Reggiannini M., Martinelli M., Papini O., Zacchetti L., Domenichetti F., Pieri G.
This paper introduces computer vision methods for detecting, recognising, and estimating Nephrops norvegicus (Norway lobster) burrow density via Underwater Television surveys. The current manual approach involves human operators visually assessing videos, which is prone to errors and subjectivity. Automated machine learning systems show promise in identifying and counting burrows, potentially standardising recognition and reducing operator errors. However, challenges exist in implementing computer vision techniques. An automated system aims to process video streams, detect seabed openings, extract visual features, and classify N. norvegicus burrows, significantly advancing the automation of underwater video reading. The primary processing presented in the paper lies in a boosting algorithm capable of extending the original annotated ground truth and assessing the improved performance of the extended data set with respect to the original one.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15509, pp. 282-295. Riva del Sole Resort & SPA - Castiglione della Pescaia, Toscana, 22-25/09/2024
DOI: 10.1007/978-3-031-82484-5_21
Project(s): NAUTILOS
Metrics:
Reggiannini M., Martinelli M., Papini O., Zacchetti L., Domenichetti F., Pieri G.
This paper introduces computer vision methods for detecting, recognising, and estimating Nephrops norvegicus (Norway lobster) burrow density via Underwater Television surveys. The current manual approach involves human operators visually assessing videos, which is prone to errors and subjectivity. Automated machine learning systems show promise in identifying and counting burrows, potentially standardising recognition and reducing operator errors. However, challenges exist in implementing computer vision techniques. An automated system aims to process video streams, detect seabed openings, extract visual features, and classify N. norvegicus burrows, significantly advancing the automation of underwater video reading. The primary processing presented in the paper lies in a boosting algorithm capable of extending the original annotated ground truth and assessing the improved performance of the extended data set with respect to the original one.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15509, pp. 282-295. Riva del Sole Resort & SPA - Castiglione della Pescaia, Toscana, 22-25/09/2024
DOI: 10.1007/978-3-031-82484-5_21
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Advancing automated detection of Nephrops norvegicus burrows in underwater television surveys through machine learning
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
The paper introduces computer vision methods for automating the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. This approach aims to improve accuracy, reduce human errors, and standardize the current manual video analysis process. By using machine learning techniques, the system can automatically process video streams and detect N. norvegicus burrow openings on the seabed. The work also explores the use of data augmentation algorithms to extend the annotated data set, enhancing the performance of the automated system compared to the original manual annotations.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 34 (issue 4), pp. 1030-1036
DOI: 10.1134/s1054661824701062
Project(s): NAUTILOS
Metrics:
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
The paper introduces computer vision methods for automating the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. This approach aims to improve accuracy, reduce human errors, and standardize the current manual video analysis process. By using machine learning techniques, the system can automatically process video streams and detect N. norvegicus burrow openings on the seabed. The work also explores the use of data augmentation algorithms to extend the annotated data set, enhancing the performance of the automated system compared to the original manual annotations.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 34 (issue 4), pp. 1030-1036
DOI: 10.1134/s1054661824701062
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2025
Other
Open Access
D7.7 Report on final reached TRL of NAUTILOS technological products
Ntoumas M., Martinelli M., Pieri G.
This report summarises the technology maturity level achieved during the NAUTILOS project.DOI: 10.5281/zenodo.18244791
Project(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
Metrics:
Ntoumas M., Martinelli M., Pieri G.
This report summarises the technology maturity level achieved during the NAUTILOS project.DOI: 10.5281/zenodo.18244791
Project(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
Metrics:
2025
Other
Open Access
Guidelines for the annotation of Nephrops norvegicus UWTV videos
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
This document describes a methodology conceived to create ground truth datasets that may be exploited in the implementation of object detection and classification algorithms tailored on the Nephrops norvegicus. In fact, supervised machine learning algorithms usually require considerable amounts of annotated data to carry out the training stage. The greater the size of the annotated dataset, the stronger the required effort from the annotators.DOI: 10.32079/isti-tr-2025/009
DOI: 10.5281/zenodo.14973160
DOI: 10.5281/zenodo.14973159
Project(s): NAUTILOS
Metrics:
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
This document describes a methodology conceived to create ground truth datasets that may be exploited in the implementation of object detection and classification algorithms tailored on the Nephrops norvegicus. In fact, supervised machine learning algorithms usually require considerable amounts of annotated data to carry out the training stage. The greater the size of the annotated dataset, the stronger the required effort from the annotators.DOI: 10.32079/isti-tr-2025/009
DOI: 10.5281/zenodo.14973160
DOI: 10.5281/zenodo.14973159
Project(s): NAUTILOS
Metrics:
2024
Conference article
Metadata Only Access
BEYOND CLIMATE CHANGE: SUSTAINED OBSERVATION IN SUPPORT OF THE BLUE ECONOMY. NAUTILOS Project
Pieri Gabriele
DELIVERING THE SCIENCE WE NEED FOR THE OCEAN WE WANT. Ocean observation is crucial in supporting economic activities related to the ocean, such as providing valuable data on resources like food, minerals, and energy This data can support sustainable management practices and ensure the continued availability of these resources for future generations. The development of new technologies and industries related to ocean monitoring and energy, like wave and tidal energy, can be driven by sustained observation, which provides a comprehensive understanding of ocean conditions and processesProject(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
Pieri Gabriele
DELIVERING THE SCIENCE WE NEED FOR THE OCEAN WE WANT. Ocean observation is crucial in supporting economic activities related to the ocean, such as providing valuable data on resources like food, minerals, and energy This data can support sustainable management practices and ensure the continued availability of these resources for future generations. The development of new technologies and industries related to ocean monitoring and energy, like wave and tidal energy, can be driven by sustained observation, which provides a comprehensive understanding of ocean conditions and processesProject(s): New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation
See at:
CNR IRIS
2024
Conference article
Open Access
A mesoscale events classifier for sea surface temperature data
Reggiannini M., Papini O., Pieri G.
The identification of mesoscale phenomena, such as upwelling, countercurrents and filaments, is an important task for oceanographers. Indeed, the occurrence of such processes involves variations in the density of nutrients which, in turn, influences the biological parameters of the habitat. In this work, we describe a novel method for an automatic classification system, the Mesoscale Events Classifier (MEC), dedicated to recognising marine mesoscale events. MEC is devoted to the study of these phenomena through the analysis of Sea Surface Temperature (SST) images captured by satellite missions.Source: MISCELLANEA INGV, vol. 80, pp. 317-319. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/122
DOI: https://commons.datacite.org/doi.org/10.13127/misc/80/122
Project(s): NAUTILOS
Metrics:
Reggiannini M., Papini O., Pieri G.
The identification of mesoscale phenomena, such as upwelling, countercurrents and filaments, is an important task for oceanographers. Indeed, the occurrence of such processes involves variations in the density of nutrients which, in turn, influences the biological parameters of the habitat. In this work, we describe a novel method for an automatic classification system, the Mesoscale Events Classifier (MEC), dedicated to recognising marine mesoscale events. MEC is devoted to the study of these phenomena through the analysis of Sea Surface Temperature (SST) images captured by satellite missions.Source: MISCELLANEA INGV, vol. 80, pp. 317-319. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/122
DOI: https://commons.datacite.org/doi.org/10.13127/misc/80/122
Project(s): NAUTILOS
Metrics:
2024
Conference article
Open Access
Advancing sustainability: research initiatives at the Signals and Images Lab
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
NAUTILOS Data Management Infrastructure
Pieri G., Tampucci M., Volpini F., Alba M., Misurale F., Novellino A.
NAUTILOS Project is an H2020 project devoted to fill-in marine observation and modelling gaps for chemical, biological and deep ocean physics variables through the development of a new generation of cost-effective sensors and samplers. Its infrastructure is organized with a data layer designed to manage data and data products, a service layer to organize them to offer the services, and an application layer i.e., the end-user interface (and features) to access and use the developed and provided services.Source: MISCELLANEA INGV, vol. 80, pp. 344-346. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/132
DOI: https://commons.datacite.org/doi.org/10.13127/misc/80/132
Project(s): NAUTILOS
Metrics:
Pieri G., Tampucci M., Volpini F., Alba M., Misurale F., Novellino A.
NAUTILOS Project is an H2020 project devoted to fill-in marine observation and modelling gaps for chemical, biological and deep ocean physics variables through the development of a new generation of cost-effective sensors and samplers. Its infrastructure is organized with a data layer designed to manage data and data products, a service layer to organize them to offer the services, and an application layer i.e., the end-user interface (and features) to access and use the developed and provided services.Source: MISCELLANEA INGV, vol. 80, pp. 344-346. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/132
DOI: https://commons.datacite.org/doi.org/10.13127/misc/80/132
Project(s): NAUTILOS
Metrics:
2024
Other
Open Access
NAUTILOS Citizen Science App
Tampucci M., Chatzinikolaou E., Keklikoglou K., Pieri G.
NAUTILOS application is a tool able to send reports, resulting from the campaigns, to a dedicated Erddap data server through a set of HTTP services.Source: MISCELLANEA INGV, vol. 80. Bergen, Norvegia, 27-29/05/2024
Project(s): NAUTILOS
Tampucci M., Chatzinikolaou E., Keklikoglou K., Pieri G.
NAUTILOS application is a tool able to send reports, resulting from the campaigns, to a dedicated Erddap data server through a set of HTTP services.Source: MISCELLANEA INGV, vol. 80. Bergen, Norvegia, 27-29/05/2024
Project(s): NAUTILOS
See at:
CNR IRIS | share.ifremer.fr | CNR IRIS
2024
Conference article
Open Access
NAUTILOS Citizen Science App
Tampucci M., Chatzinikolaou E., Keklikoglou K., Pieri G.
Citizen Science projects including crowd sourcing of plastic litter data have been implemented successfully in many countries and within several long term monitoring projects. The involvement of local communities in the collection of data gives them the opportunity to actively participate in the problem’s solution and has the potential to raise environmental awareness. Within NAUTILOS Project, many different partners are involved in Citizen Science (CS) campaigns. Each one does not necessarily perform the same type of activities or focus on the same kind of citizens or citizens organisations. The primary goal of this activity is to develop an App which can support various CS activities arranged by different partners in the NAUTILOS project and with different focuses.Source: MISCELLANEA INGV, vol. 80, pp. 349-351. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/134
DOI: https://commons.datacite.org/doi.org/10.13127/misc/80/134
Project(s): NAUTILOS
Metrics:
Tampucci M., Chatzinikolaou E., Keklikoglou K., Pieri G.
Citizen Science projects including crowd sourcing of plastic litter data have been implemented successfully in many countries and within several long term monitoring projects. The involvement of local communities in the collection of data gives them the opportunity to actively participate in the problem’s solution and has the potential to raise environmental awareness. Within NAUTILOS Project, many different partners are involved in Citizen Science (CS) campaigns. Each one does not necessarily perform the same type of activities or focus on the same kind of citizens or citizens organisations. The primary goal of this activity is to develop an App which can support various CS activities arranged by different partners in the NAUTILOS project and with different focuses.Source: MISCELLANEA INGV, vol. 80, pp. 349-351. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/134
DOI: https://commons.datacite.org/doi.org/10.13127/misc/80/134
Project(s): NAUTILOS
Metrics:
2024
Other
Open Access
A mesoscale events classifier for sea surface temperature data
Reggiannini M., Papini O., Pieri G.
The Mesoscale Events Classifier (MEC) is a tool that has been developed to detect and classify patterns of mesoscale events in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data.Source: MISCELLANEA INGV, vol. 80. Bergen, Norvegia, 27-29/05/2024
Project(s): NAUTILOS
Reggiannini M., Papini O., Pieri G.
The Mesoscale Events Classifier (MEC) is a tool that has been developed to detect and classify patterns of mesoscale events in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data.Source: MISCELLANEA INGV, vol. 80. Bergen, Norvegia, 27-29/05/2024
Project(s): NAUTILOS
See at:
CNR IRIS | share.ifremer.fr | CNR IRIS
2024
Other
Open Access
NAUTILOS Data Management Infrastructure
Pieri G., Tampucci M., Volpini F., Alba M., Misurale F., Novellino A.
NAUTILOS Project is an H2020 project devoted to fill-in marine observation and modelling gaps for chemical, biological and deep ocean physics variables through the development of a new generation of cost-effective sensors and samplers. Its infrastructure is organized with a data layer designed to manage data and data products, a service layer to organize them to offer the services, and an application layer i.e., the end-user interface (and features) to access and use the developed and provided services.Source: MISCELLANEA INGV, vol. 80. Bergen (Norway), 27-29/05/2024
Project(s): NAUTILOS
Pieri G., Tampucci M., Volpini F., Alba M., Misurale F., Novellino A.
NAUTILOS Project is an H2020 project devoted to fill-in marine observation and modelling gaps for chemical, biological and deep ocean physics variables through the development of a new generation of cost-effective sensors and samplers. Its infrastructure is organized with a data layer designed to manage data and data products, a service layer to organize them to offer the services, and an application layer i.e., the end-user interface (and features) to access and use the developed and provided services.Source: MISCELLANEA INGV, vol. 80. Bergen (Norway), 27-29/05/2024
Project(s): NAUTILOS
See at:
CNR IRIS | share.ifremer.fr | CNR IRIS
2024
Other
Open Access
NAUTILOS' AI innovations on fisheries
Pieri Gabriele, Martinelli Michela, Sà Sandra, Granchinho Sara, Hristova Ana
NAUTILOS aims to address gaps in marine observation and modelling by developing a new generation of cost-effective sensors and samplers for essential ocean variables, including physical (salinity, temperature), chemical (inorganic carbon, nutrients, oxygen), biological (phytoplankton, zooplankton, marine mammals), and micro-/nano-plastics. These advancements will enhance our understanding of environmental changes and human impacts related to aquaculture, fisheries, and marine litter.Project(s): NAUTILOS
Pieri Gabriele, Martinelli Michela, Sà Sandra, Granchinho Sara, Hristova Ana
NAUTILOS aims to address gaps in marine observation and modelling by developing a new generation of cost-effective sensors and samplers for essential ocean variables, including physical (salinity, temperature), chemical (inorganic carbon, nutrients, oxygen), biological (phytoplankton, zooplankton, marine mammals), and micro-/nano-plastics. These advancements will enhance our understanding of environmental changes and human impacts related to aquaculture, fisheries, and marine litter.Project(s): NAUTILOS
2024
Other
Open Access
NAUTILOS D1.12 - Final Data Management Plan
Dapueto G., Novellino A., Pieri G.
D1.12: In this deliverable the final version of the DMP at M48 will be reported.DOI: 10.5281/zenodo.14283186
Project(s): NAUTILOS
Metrics:
Dapueto G., Novellino A., Pieri G.
D1.12: In this deliverable the final version of the DMP at M48 will be reported.DOI: 10.5281/zenodo.14283186
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | zenodo.org | CNR IRIS