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2021
Other
Open Access
A tool for the temporal analysis of sea surface temperature maps
Papini O
This document describes the usage of a tool that produces plots of the evolution of the sea surface temperature in a specified space-time window, extracting data from a series of NetCDF files.DOI: 10.32079/isti-tr-2021/011
Project(s): NAUTILOS
Metrics:
Papini O
This document describes the usage of a tool that produces plots of the evolution of the sea surface temperature in a specified space-time window, extracting data from a series of NetCDF files.DOI: 10.32079/isti-tr-2021/011
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS 
| ISTI Repository | CNR IRIS 
2022
Other
Open Access
SpaghettiData and SpaghettiPlot: two Python classes for analysing and visualising SST trends
Papini O
This document describes the formalization of a "spaghetti plot" (i.e. a graph that captures the sea surface temperature trends in a target area) as a Python object, for which we defined two custom classes (SpaghettiData and SpaghettiPlot). In particular, we list the attributes and methods of these classes, together with the utilities that we use to create objects belonging to them.DOI: 10.32079/isti-tr-2022/001
Project(s): NAUTILOS
Metrics:
Papini O
This document describes the formalization of a "spaghetti plot" (i.e. a graph that captures the sea surface temperature trends in a target area) as a Python object, for which we defined two custom classes (SpaghettiData and SpaghettiPlot). In particular, we list the attributes and methods of these classes, together with the utilities that we use to create objects belonging to them.DOI: 10.32079/isti-tr-2022/001
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2023
Software
Metadata Only Access
MEC: Mesoscale Events Classifier
Papini O
This software consists of a Python 3 implementation of the Mesoscale Events Classifier (MEC) algorithm, which has been developed as part of the activities of Task 8.5 of the NAUTILOS project. The algorithm uses Sea Surface Temperature data coming from satellite missions to detect and classify patterns associated with "mesoscale events" in an upwelling ecosystem.Project(s): NAUTILOS
Papini O
This software consists of a Python 3 implementation of the Mesoscale Events Classifier (MEC) algorithm, which has been developed as part of the activities of Task 8.5 of the NAUTILOS project. The algorithm uses Sea Surface Temperature data coming from satellite missions to detect and classify patterns associated with "mesoscale events" in an upwelling ecosystem.Project(s): NAUTILOS
See at:
github.com | CNR IRIS
2023
Other
Open Access
Mesoscale Events Classifier: an algorithm for the detection and classification of upwelling events using Sea Surface Temperature satellite data
Papini O
In two previous technical reports we described a tool that produces a so-called spaghetti plot, i.e. a plot that is able to capture the trends of the sea surface temperature (SST) in a chosen time interval and within a target area; and the formalization of spaghetti plots through the definition of two custom Python 3 classes. In this report we outline an algorithm that uses SST data to detect and classify mesoscale upwelling events. In particular, the algorithm (called Mesoscale Events Classifier, MEC) takes as input the SST data organized as a SpaghettiData dictionary and returns a map of the area of interest where the zones in which the algorithm detects an event are highlighted and labelled with an event type.DOI: 10.32079/isti-tr-2023/011
Project(s): NAUTILOS
Metrics:
Papini O
In two previous technical reports we described a tool that produces a so-called spaghetti plot, i.e. a plot that is able to capture the trends of the sea surface temperature (SST) in a chosen time interval and within a target area; and the formalization of spaghetti plots through the definition of two custom Python 3 classes. In this report we outline an algorithm that uses SST data to detect and classify mesoscale upwelling events. In particular, the algorithm (called Mesoscale Events Classifier, MEC) takes as input the SST data organized as a SpaghettiData dictionary and returns a map of the area of interest where the zones in which the algorithm detects an event are highlighted and labelled with an event type.DOI: 10.32079/isti-tr-2023/011
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2024
Journal article
Open Access
A basis for the cohomology of compact models of toric arrangements
Gaiffi G, Papini O., Siconolfi V.
In this paper we find monomial bases for the integer cohomology rings of compact wonderful models of toric arrangements. In the description of the monomials various combinatorial objects come into play: building sets, nested sets, and the fan of a suitable toric variety. We provide some examples computed via a SageMath program and then we focus on the case of the toric arrangements associated with root systems of type A. Here the combinatorial description of our basis offers a geometrical point of view on the relation between some Eulerian statistics on the symmetric group.Source: PURE AND APPLIED MATHEMATICS QUARTERLY, vol. 20 (issue 1), pp. 427-470
DOI: 10.4310/pamq.2024.v20.n1.a9
DOI: 10.48550/arxiv.2205.00443
Metrics:
Gaiffi G, Papini O., Siconolfi V.
In this paper we find monomial bases for the integer cohomology rings of compact wonderful models of toric arrangements. In the description of the monomials various combinatorial objects come into play: building sets, nested sets, and the fan of a suitable toric variety. We provide some examples computed via a SageMath program and then we focus on the case of the toric arrangements associated with root systems of type A. Here the combinatorial description of our basis offers a geometrical point of view on the relation between some Eulerian statistics on the symmetric group.Source: PURE AND APPLIED MATHEMATICS QUARTERLY, vol. 20 (issue 1), pp. 427-470
DOI: 10.4310/pamq.2024.v20.n1.a9
DOI: 10.48550/arxiv.2205.00443
Metrics:
See at:
arXiv.org e-Print Archive | IRIS Cnr | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Pure and Applied Mathematics Quarterly | doi.org | Archivio della Ricerca - Università di Pisa | Archivio Istituzionale della Ricerca - Politecnico di Bari | CNR IRIS | CNR IRIS
2021
Conference article
Open Access
Mesoscale patterns identification through SST image processing
Reggiannini M, Janeiro J, Martins F, Papini O, Pieri G
Mesoscale marine phenomena represent important features to understand and include within predictive models, which provide valuable information for proper environmental policy making. For example the rearrangement of the organic substances, consequent to the dynamics of the water masses affected by the mentioned phenomena, meaningfully modifies the actual condition of local habitats. Indeed it may facilitate the onset of non resident living species at the expense of resident ones, eventually affecting related human activity, such as commercial fishery. Objective of this work is the detection and identification of mesoscale events, in terms of specific marine surface patterns that are observed throughout such events, e.g. water filaments, countercurrents, meanders due to upwelling wind actions stress. These phenomena can be studied and monitored through the analysis of Sea Surface Temperature images captured by satellite missions, such as Metop, and MODIS Terra/Aqua. A quantitative description of such events is proposed, based on dedicated algorithms that extract temporal and spatial features from the images, and exploit them to provide a signature discriminating different observed scenarios. Preliminary results of the application of the proposed approach to a dataset related to the southwestern region of the Iberian Peninsula are presented.DOI: 10.5220/0010714600003061
Project(s): NAUTILOS
Metrics:
Reggiannini M, Janeiro J, Martins F, Papini O, Pieri G
Mesoscale marine phenomena represent important features to understand and include within predictive models, which provide valuable information for proper environmental policy making. For example the rearrangement of the organic substances, consequent to the dynamics of the water masses affected by the mentioned phenomena, meaningfully modifies the actual condition of local habitats. Indeed it may facilitate the onset of non resident living species at the expense of resident ones, eventually affecting related human activity, such as commercial fishery. Objective of this work is the detection and identification of mesoscale events, in terms of specific marine surface patterns that are observed throughout such events, e.g. water filaments, countercurrents, meanders due to upwelling wind actions stress. These phenomena can be studied and monitored through the analysis of Sea Surface Temperature images captured by satellite missions, such as Metop, and MODIS Terra/Aqua. A quantitative description of such events is proposed, based on dedicated algorithms that extract temporal and spatial features from the images, and exploit them to provide a signature discriminating different observed scenarios. Preliminary results of the application of the proposed approach to a dataset related to the southwestern region of the Iberian Peninsula are presented.DOI: 10.5220/0010714600003061
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ISTI Repository | www.scitepress.org | CNR IRIS
2021
Journal article
Open Access
SST image processing for mesoscale patterns identification
Papini O, Reggiannini M, Pieri G
Understanding the marine environment dynamics to accordingly design computational predictive tools represents a factor of paramount relevance to implement suitable policy plans. In this framework mesoscale marine events are important to study and understand since human related activities, such as commercial fishery, strongly depend on this type of phenomena. Indeed the dynamics of water masses affect the local habitats due to nutrients and organic substances transport, interfering with the fauna and flora development processes. Mesoscale events can be classified based on the presence of specific hydrodynamics features, such as water filaments, counter-currents or meanders originating from upwelling wind actions stress. In this paper a novel method to study these phenomena is proposed, based on the analysis of Sea Surface Temperature imagery captured by satellite missions (Metop, MODIS Terra/Aqua). Dedicated algorithms are presented, with the goal to detect and identify different observed scenarios based on the extraction and analysis of discriminating quantitative features. Promising results returned by the application of the proposed method to data captured within the maritime region in front of the southwestern Iberian coasts are presented.Source: ENGINEERING PROCEEDINGS, vol. 8 (issue 1)
DOI: 10.3390/engproc2021008005
Project(s): NAUTILOS
Metrics:
Papini O, Reggiannini M, Pieri G
Understanding the marine environment dynamics to accordingly design computational predictive tools represents a factor of paramount relevance to implement suitable policy plans. In this framework mesoscale marine events are important to study and understand since human related activities, such as commercial fishery, strongly depend on this type of phenomena. Indeed the dynamics of water masses affect the local habitats due to nutrients and organic substances transport, interfering with the fauna and flora development processes. Mesoscale events can be classified based on the presence of specific hydrodynamics features, such as water filaments, counter-currents or meanders originating from upwelling wind actions stress. In this paper a novel method to study these phenomena is proposed, based on the analysis of Sea Surface Temperature imagery captured by satellite missions (Metop, MODIS Terra/Aqua). Dedicated algorithms are presented, with the goal to detect and identify different observed scenarios based on the extraction and analysis of discriminating quantitative features. Promising results returned by the application of the proposed method to data captured within the maritime region in front of the southwestern Iberian coasts are presented.Source: ENGINEERING PROCEEDINGS, vol. 8 (issue 1)
DOI: 10.3390/engproc2021008005
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ISTI Repository | ISTI Repository | www.mdpi.com | CNR IRIS | CNR IRIS
2021
Contribution to conference
Open Access
Image processing applied to temperature pattern identification
Papini O, Pieri G, Reggiannini M
The objective of our work is to detect and classify mesoscale patterns in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data. The poster shows how we organize this information in a "spaghetti plot", a tool that we use to analyse different trends of the SST in a target area for a period of time, and how we can associate those trends with different mesoscale patterns.Project(s): NAUTILOS
Papini O, Pieri G, Reggiannini M
The objective of our work is to detect and classify mesoscale patterns in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data. The poster shows how we organize this information in a "spaghetti plot", a tool that we use to analyse different trends of the SST in a target area for a period of time, and how we can associate those trends with different mesoscale patterns.Project(s): NAUTILOS
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
NAUTILOS - Automatic image analysis tools
Pieri G, Reggiannini M, Papini O
This deliverable will consist of the implementation of image analysis tools based on methods and algorithms designed explicitly to perform different automatic classifications. These tools will be used and applied both on already available and acquired images during the project. An accompanying report describing the tools will be produced.Project(s): NAUTILOS
Pieri G, Reggiannini M, Papini O
This deliverable will consist of the implementation of image analysis tools based on methods and algorithms designed explicitly to perform different automatic classifications. These tools will be used and applied both on already available and acquired images during the project. An accompanying report describing the tools will be produced.Project(s): NAUTILOS
See at:
CNR IRIS | ISTI Repository | zenodo.org | CNR IRIS
2022
Journal article
Open Access
An automated analysis tool for the classification of sea surface temperature imagery
Reggiannini M, Papini O, Pieri G
Sea observation through remote sensing technologies plays an essential role in understanding the health status of the marine coastal environment, its fauna species and their future behavior. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows us to perform predictions and adopt proper decisions. This paper concerns the proposal of a classification system devoted to recognizing marine mesoscale events. These phenomena are studied and monitored by analyzing sea surface temperature imagery. Currently, the standard way to perform such analysis relies on experts manually visualizing, analyzing, and tagging large imagery datasets. Nowadays, the availability of remote sensing data has increased so much that it is desirable to replace the labor-intensive, time-consuming, and subjective manual interpretation with automated analysis tools. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 32 (issue 3), pp. 631-635
DOI: 10.1134/s1054661822030336
Project(s): NAUTILOS
Metrics:
Reggiannini M, Papini O, Pieri G
Sea observation through remote sensing technologies plays an essential role in understanding the health status of the marine coastal environment, its fauna species and their future behavior. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows us to perform predictions and adopt proper decisions. This paper concerns the proposal of a classification system devoted to recognizing marine mesoscale events. These phenomena are studied and monitored by analyzing sea surface temperature imagery. Currently, the standard way to perform such analysis relies on experts manually visualizing, analyzing, and tagging large imagery datasets. Nowadays, the availability of remote sensing data has increased so much that it is desirable to replace the labor-intensive, time-consuming, and subjective manual interpretation with automated analysis tools. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 32 (issue 3), pp. 631-635
DOI: 10.1134/s1054661822030336
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2023
Contribution to book
Open Access
Mesoscale events classification in sea surface temperature imagery
Reggiannini M, Janeiro J, Martins F, Papini O, Pieri G
Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.DOI: 10.1007/978-3-031-25599-1_38
Project(s): NAUTILOS
Metrics:
Reggiannini M, Janeiro J, Martins F, Papini O, Pieri G
Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.DOI: 10.1007/978-3-031-25599-1_38
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
Automated image processing for remote sensing data classification
Reggiannini M, Papini O, Pieri G
Remote sensing technologies allow for continuous and valuable monitoring of the Earth's various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.DOI: 10.1007/978-3-031-37742-6_43
Project(s): NAUTILOS
Metrics:
Reggiannini M, Papini O, Pieri G
Remote sensing technologies allow for continuous and valuable monitoring of the Earth's various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.DOI: 10.1007/978-3-031-37742-6_43
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
MEC: a Mesoscale Events Classifier for oceanographic imagery
Pieri G, Janeiro J, Martins F, Papini O, Reggiannini M
The observation of the sea through remote sensing technologies plays a fundamental role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.Source: APPLIED SCIENCES, vol. 13 (issue 3)
DOI: 10.3390/app13031565
Project(s): NAUTILOS
Metrics:
Pieri G, Janeiro J, Martins F, Papini O, Reggiannini M
The observation of the sea through remote sensing technologies plays a fundamental role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.Source: APPLIED SCIENCES, vol. 13 (issue 3)
DOI: 10.3390/app13031565
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2023
Conference article
Open Access
Evaluation of a marine mesoscale events classifier
Reggiannini M, Papini O, Pieri G
Marine mesoscale phenomena are relevant oceanographic processes that impact on fishery, biodiversity and climate variation. In previous literature, their analysis has been tackled by processing instantaneous remote sensing observations and returning a classification of the observed event. Indeed, these phenomena occur within an extended time range, thus an analysis including time dependence is desirable. Mesoscale Events Classifier (MEC) is an algorithm devoted to the classification of marine mesoscale events in sea surface temperature imagery. By processing time series of satellite temperature observations MEC recognizes the considered area of interest as the domain of one out of a given number of possible events and returns the corresponding label. Objective of this work is to discuss the performance of the MEC pipeline in terms of its capability of correctly capturing the nature of the observed mesoscale process. The evaluation process exploited satellite remote sensing data collected in front of the Portuguese coast.DOI: 10.1109/icasspw59220.2023.10193234
Project(s): NAUTILOS
Metrics:
Reggiannini M, Papini O, Pieri G
Marine mesoscale phenomena are relevant oceanographic processes that impact on fishery, biodiversity and climate variation. In previous literature, their analysis has been tackled by processing instantaneous remote sensing observations and returning a classification of the observed event. Indeed, these phenomena occur within an extended time range, thus an analysis including time dependence is desirable. Mesoscale Events Classifier (MEC) is an algorithm devoted to the classification of marine mesoscale events in sea surface temperature imagery. By processing time series of satellite temperature observations MEC recognizes the considered area of interest as the domain of one out of a given number of possible events and returns the corresponding label. Objective of this work is to discuss the performance of the MEC pipeline in terms of its capability of correctly capturing the nature of the observed mesoscale process. The evaluation process exploited satellite remote sensing data collected in front of the Portuguese coast.DOI: 10.1109/icasspw59220.2023.10193234
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2024
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.Project(s): NAUTILOS
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.Project(s): NAUTILOS
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
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
Project(s): NAUTILOS
Metrics:
not yet published
Conference article
Open Access
Machine Learning Approaches for Automated Detection of Nephrops norvegicus Burrows in Underwater Surveys
Oscar Papini, Enrico Cecapolli, Filippo Domenichetti, Michela Martinelli, Gabriele Pieri, Marco Reggiannini, Lorenzo Zacchetti
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. Kolkata, India, 01-05/12/2024
Project(s): NAUTILOS
Oscar Papini, Enrico Cecapolli, Filippo Domenichetti, Michela Martinelli, Gabriele Pieri, Marco Reggiannini, Lorenzo Zacchetti
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. Kolkata, India, 01-05/12/2024
Project(s): NAUTILOS
2025
Contribution to book
Metadata Only Access
Computer vision to support Nephrops norvegicus imagery annotation
Marco Reggiannini, Enrico Cecapolli, Filippo Domenichetti, Michela Martinelli, Oscar Papini, Gabriele Pieri, Lorenzo Zacchetti
This document reports about the implementation of a computer vision procedure to estimate Nephrops norvegicus burrows density by analysing Underwater Television (UWTV) surveys. This activity, developed in cooperation with the ICES WGNEPS group, aims at providing an automatic system to support (i) the detection of the N. norvegicus openings, (ii) their grouping into systems (i.e. burrows) and (iii) the count of the distinct burrows. This could represent a relevant tool to simplify and optimise the stock assessment process.Source: ICES SCIENTIFIC REPORTS, pp. 23-28
DOI: 10.17895/ices.pub.28652012
Project(s): NAUTILOS
Metrics:
Marco Reggiannini, Enrico Cecapolli, Filippo Domenichetti, Michela Martinelli, Oscar Papini, Gabriele Pieri, Lorenzo Zacchetti
This document reports about the implementation of a computer vision procedure to estimate Nephrops norvegicus burrows density by analysing Underwater Television (UWTV) surveys. This activity, developed in cooperation with the ICES WGNEPS group, aims at providing an automatic system to support (i) the detection of the N. norvegicus openings, (ii) their grouping into systems (i.e. burrows) and (iii) the count of the distinct burrows. This could represent a relevant tool to simplify and optimise the stock assessment process.Source: ICES SCIENTIFIC REPORTS, pp. 23-28
DOI: 10.17895/ices.pub.28652012
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS
2024
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)
Project(s): NAUTILOS
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)
Project(s): NAUTILOS
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:
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