Sonder les zones inaccessibles avec la bouée bathymétrique HydroBall®

International audienceThis paper describes the performance analysis of an autonomous drifting buoy equipped with a GNSS receiver, an inertial measurement unit and a single beam echosounder. The system is intended for surveying difficult access areas like high-flowing rivers, confined zones and ultra shallow waters, which are unreachable using a classical survey launches. Thanks to a total propagated uncertainty analysis, we show that the system meets international and industrial hydrographic standards

Apprentissage automatique de données massives bathymétriques pour l'optimisation de systèmes de levé hydrographique

The mission of hydrographic services are to know and describe the physical marine environment in its interactions with the atmosphere, the seabed and coastal areas, to forecast its evolution and to ensure the dissemination of the corresponding informations. Within the framework of these missions, of which the safety of navigation is the key mission, they carry out campaigns at sea in order to acquire the maximum of bathymetric and oceanographic informations on a precise zone. In this manuscript, we propose different methods to facilitate the daily work of operators, by studying different levels of scales: micro, meso and macro. The micro level focuses on the data, and thus for our case the bathymetric soundings, in order to extract the maximum added value possible. The meso level will build from this bathymetric data the right information to detect outliers data via machine learning methods in bathymetric data. Finally, the macro level concentrates in the qualification of the survey in its entirety while taking into account the preferences of the end user via multiple-criteria decision analysis method.Les services hydrographiques ont pour pour mission de connaître et décrire l’environnement physique marin dans ses relations avec l’atmosphère, les fonds marins et les zones littorales, d’en prévoir l’évolution et d’assurer la diffusion des informations correspondantes. Dans le cadre de ces missions, dont la sécurité de la navigation est la mission fondamentale, ils réalisent des campagnes à la mer afin d’acquérir le maximum d’informations bathymétriques et océanographiques sur une zone précise. Dans ce manuscrit, nous proposons différentes méthodes visant à faciliter le travail quotidien des opérateurs en considérant différents niveaux d'échelles: micro, meso et macro. Le niveau micro touche à la donnée et donc dans notre cas à la sonde bathymétrique afin d'en extraire le maximum de valeur ajoutée possible. Le niveau meso construit à partir de cette donnée bathymétrique les bonnes informations permettant de détecter des données aberrantes via des méthodes d'apprentissage machine dans les lots de données bathymétriques. Enfin, le niveau macro s'attache à la qualification du levé dans son intégralité tout en prenant en compte les préférences de l'utilisateur final via des méthodes d'aide à la décision multicritère

Apprentissage automatique de données massives bathymétriques pour l'optimisation de systèmes de levé hydrographique

The mission of hydrographic services are to know and describe the physical marine environment in its interactions with the atmosphere, the seabed and coastal areas, to forecast its evolution and to ensure the dissemination of the corresponding informations. Within the framework of these missions, of which the safety of navigation is the key mission, they carry out campaigns at sea in order to acquire the maximum of bathymetric and oceanographic informations on a precise zone. In this manuscript, we propose different methods to facilitate the daily work of operators, by studying different levels of scales: micro, meso and macro. The micro level focuses on the data, and thus for our case the bathymetric soundings, in order to extract the maximum added value possible. The meso level will build from this bathymetric data the right information to detect outliers data via machine learning methods in bathymetric data. Finally, the macro level concentrates in the qualification of the survey in its entirety while taking into account the preferences of the end user via multiple-criteria decision analysis method.Les services hydrographiques ont pour pour mission de connaître et décrire l’environnement physique marin dans ses relations avec l’atmosphère, les fonds marins et les zones littorales, d’en prévoir l’évolution et d’assurer la diffusion des informations correspondantes. Dans le cadre de ces missions, dont la sécurité de la navigation est la mission fondamentale, ils réalisent des campagnes à la mer afin d’acquérir le maximum d’informations bathymétriques et océanographiques sur une zone précise. Dans ce manuscrit, nous proposons différentes méthodes visant à faciliter le travail quotidien des opérateurs en considérant différents niveaux d'échelles: micro, meso et macro. Le niveau micro touche à la donnée et donc dans notre cas à la sonde bathymétrique afin d'en extraire le maximum de valeur ajoutée possible. Le niveau meso construit à partir de cette donnée bathymétrique les bonnes informations permettant de détecter des données aberrantes via des méthodes d'apprentissage machine dans les lots de données bathymétriques. Enfin, le niveau macro s'attache à la qualification du levé dans son intégralité tout en prenant en compte les préférences de l'utilisateur final via des méthodes d'aide à la décision multicritère

Machine learning on massive bathymetric data for the optimization of hydrographic survey systems

Les services hydrographiques ont pour pour mission de connaître et décrire l’environnement physique marin dans ses relations avec l’atmosphère, les fonds marins et les zones littorales, d’en prévoir l’évolution et d’assurer la diffusion des informations correspondantes. Dans le cadre de ces missions, dont la sécurité de la navigation est la mission fondamentale, ils réalisent des campagnes à la mer afin d’acquérir le maximum d’informations bathymétriques et océanographiques sur une zone précise. Dans ce manuscrit, nous proposons différentes méthodes visant à faciliter le travail quotidien des opérateurs en considérant différents niveaux d'échelles: micro, meso et macro. Le niveau micro touche à la donnée et donc dans notre cas à la sonde bathymétrique afin d'en extraire le maximum de valeur ajoutée possible. Le niveau meso construit à partir de cette donnée bathymétrique les bonnes informations permettant de détecter des données aberrantes via des méthodes d'apprentissage machine dans les lots de données bathymétriques. Enfin, le niveau macro s'attache à la qualification du levé dans son intégralité tout en prenant en compte les préférences de l'utilisateur final via des méthodes d'aide à la décision multicritère.The mission of hydrographic services are to know and describe the physical marine environment in its interactions with the atmosphere, the seabed and coastal areas, to forecast its evolution and to ensure the dissemination of the corresponding informations. Within the framework of these missions, of which the safety of navigation is the key mission, they carry out campaigns at sea in order to acquire the maximum of bathymetric and oceanographic informations on a precise zone. In this manuscript, we propose different methods to facilitate the daily work of operators, by studying different levels of scales: micro, meso and macro. The micro level focuses on the data, and thus for our case the bathymetric soundings, in order to extract the maximum added value possible. The meso level will build from this bathymetric data the right information to detect outliers data via machine learning methods in bathymetric data. Finally, the macro level concentrates in the qualification of the survey in its entirety while taking into account the preferences of the end user via multiple-criteria decision analysis method

A Review of Data Cleaning Approaches in a Hydrographic Framework with a Focus on Bathymetric Multibeam Echosounder Datasets

International audienceAutomatic cleaning of MultiBeam EchoSounder (MBES) bathymetric datasets is a critical issue in data processing especially with the objective of nautical charting. A number of approaches have already been investigated in order to provide solution in views of operationally reaching this still challenging problem. This paper aims at providing a comprehensive and structured overview of existing contributions in the literature. For this purpose, a taxonomy is proposed to categorize the whole set of automatic and semi-automatic methods addressing MBES data cleaning. The non-supervised algorithms that compose the majority of the methods developed in the hydrographic field, are mainly described according to both the features of the bathymetric data and the type of outliers to detect. Based on this detailed review, past and future developments are discussed in light of both implementation and test on datasets and metrics used for performances assessment

Automatic Data Quality Assessment of Hydrographic Surveys Taking Into Account Experts' Preferences

International audienceData quality assessment of hydrographic surveys is a complex problem, since context dependent acquisition conditions using multiple sensors contribute to numerous data imperfections, which have different consequences depending on the intended final product. In this work we propose a generic methodology integrating experts' preferences through multi-criteria preference models with data quality techniques to generate explainable overall quality assessments of hydrographic surveys which depend on the expected end-uses. Four hydrographic surveys of different geographic locations and contrasting characteristics were studied, according to the preferences of an acoustician, an oceanographer, and a hydrographer. The obtained results indicate that each survey was appropriately evaluated, indicating the reasons that led to the specific assessment. Index Terms-hydrographic survey, data quality assessment, preference modelling, multi-criteria decision aiding

Exploration du lac de Guerlédan : présentation des projets étudiants en hydrographie et acoustique sous-marine

National audienceLe projet d’exploration du lac de Guerlédan est un projet porté par l’équipe OSM (Ocean Sensing and Mapping) de l’ENSTA Bretagne à destination de ses étudiants des filières Hydrographie/Océanographie et Robotique. L’objectif est d’amener les étudiants de dernière année (équivalent Master 2) à mettre en commun leurs connaissances aussi bien pratiques que théoriques autour d’une problématique de recherche. Grâce à de nombreux partenaires institutionnels et industriels, les étudiants ont accès à des moyens d’essais de pointe, et peuvent travailler en collaboration avec des experts des domaines concernés. Parmi les sujets proposés aux étudiants, plusieurs ont une forte composante acoustiquecomme par exemple, l’étude de la composition de la colonne d’eau à l’aide de différents systèmes sonars, l’étalonnage de la réflectivité d’un sondeur multifaisceaux, ou la reconstitution 3D de structures sous-marines par des méthodes acoustiques. Dans un premier temps, la mise en place du projet d’enseignement et ses aboutissements seront présentés, puis les résultats acoustiques des projets étudiants cités précédemment seront détaillés

Integrating user preferences in the automatic quality assessment of hydrographic surveys

International audienceThe goal of hydrographic surveys is to measure and describe features which affect maritime navigation, marine construction, dredging, and other human activities at sea. These surveys serve among other thing to create nautical charts. The International Hydrographic Organization (IHO) published recently an update of the Standards for Hydrographic Surveys. It shows how important data quality is for these surveys, by setting new standards which allow to refine the data quality levels of criteria according to stated hydrographic needs. This means that depending on the final use of a survey, its quality could be perceived differently by users. It follows from this that, evaluating the quality of a survey, while taking into account the needs of a the end user, as well as automating this evaluation, are real issues. Next to that, data and information quality are classically examined through multiple dimensions. The choice of these dimensions, as well as their relative importance in the overall assessment of the quality, do obviously depend again on the end user. These dimensions might also be influenced by the context of the survey, which in turn might be perceived differently by each user, and therefore modeled in various ways. Given the many elements that impact the quality of hydrographic surveys, we consider the following research question: How to integrate the end user's perception of the quality of a survey with the quality of the acquisition conditions, while guaranteeing a traceable and explainable overall assessment of the survey ? To answer this question, we propose a process which first uses data quality analysis techniques to automatically determine, from data and metadata, various quality measures of hydrographic surveys, and combine these with an MR-Sort preference model to integrate various user profiles into the overall assessment. We show that these multiple quality dimensions (or criteria), combined with different user profiles (acoustician, cartographer, etc.), can lead to different overall evaluations of the quality of hydrographical surveys. Last but not least, we show how these combined tools can lead to explainable outputs, which can be used to analyse existing surveys, and make recommendations to improve future ones

Integrating user preferences in the automatic quality assessment of hydrographic surveys

International audienceThe goal of hydrographic surveys is to measure and describe features which affect maritime navigation, marine construction, dredging, and other human activities at sea. These surveys serve among other thing to create nautical charts. The International Hydrographic Organization (IHO) published recently an update of the Standards for Hydrographic Surveys. It shows how important data quality is for these surveys, by setting new standards which allow to refine the data quality levels of criteria according to stated hydrographic needs. This means that depending on the final use of a survey, its quality could be perceived differently by users. It follows from this that, evaluating the quality of a survey, while taking into account the needs of a the end user, as well as automating this evaluation, are real issues. Next to that, data and information quality are classically examined through multiple dimensions. The choice of these dimensions, as well as their relative importance in the overall assessment of the quality, do obviously depend again on the end user. These dimensions might also be influenced by the context of the survey, which in turn might be perceived differently by each user, and therefore modeled in various ways. Given the many elements that impact the quality of hydrographic surveys, we consider the following research question: How to integrate the end user's perception of the quality of a survey with the quality of the acquisition conditions, while guaranteeing a traceable and explainable overall assessment of the survey ? To answer this question, we propose a process which first uses data quality analysis techniques to automatically determine, from data and metadata, various quality measures of hydrographic surveys, and combine these with an MR-Sort preference model to integrate various user profiles into the overall assessment. We show that these multiple quality dimensions (or criteria), combined with different user profiles (acoustician, cartographer, etc.), can lead to different overall evaluations of the quality of hydrographical surveys. Last but not least, we show how these combined tools can lead to explainable outputs, which can be used to analyse existing surveys, and make recommendations to improve future ones

Multibeam outlier detection by clustering and topological persistence approach, ToMATo algorithm

International audienceThe datasets acquired during hydrographic surveys contain outliers, i.e., soundings that do not describe the sea bottom. Many algorithms are developed to identify them. Here, we study unsupervised non-parametric algorithms with a densitybased approach. These algorithms make no assumption about the data and identify outliers as the data furthest away from their neighbors. We asses the ToMATo method developed by INRIA in 2009 to detect outlier soundings from multibeam echosounder data. This clustering algorithm combines a mode-seeking phase with a cluster merging phase using topological persistence. After the theoretical presentation of the ToMATo algorithm, we evaluate its performance on four data sets representing a wide variety of seabeds. We compare this method with the well-known DBSCAN and LOF algorithms. Finally, we suggest an application of the ToMATo algorithm to multibeam data acquired in extradetection mode, where topological persistence allows to form the most relevant clusters