Automatic 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.

Seafloor backscatter mosaics are now routinely produced from multibeam echosounder data and used in a wide range of marine applications. However, large differences (>5 dB) can often be observed between the mosaics produced by different software packages processing the same dataset. Without transparency of the processing pipeline and the lack of consistency between software packages raises concerns about the validity of the final results. To recognize the source(s) of inconsistency between software, it is necessary to understand at which stage(s) of the data processing chain the differences become substantial. To this end, willing commercial and academic software developers were invited to generate intermediate processed backscatter results from a common dataset, for cross-comparison. The first phase of the study requested intermediate processed results consisting of two stages of the processing sequence: the one-value-per-beam level obtained after reading the raw data and the level obtained after radiometric corrections but before compensation of the angular dependence. Both of these intermediate results showed large differences between software solutions. This study explores the possible reasons for these differences and highlights the need for collaborative efforts between software developers and their users to improve the consistency and transparency of the backscatter data processing sequence.

With the new data acquisition capabilities of latest MBES, the need of an automation data processing is more and more essential. The aim of this article is to present what kind of data processing we want to improve, what techniques were used in the past and how machine learning could help us now and in the future for a better bathymetric data processing.

With more than 300 years of existence, Shom is the oldest active hydrographic service in the world. Compiling and deconflicting this much history automatically is a real challenge. This article will present the types of data Shom has to manipulate and the different steps of the workflow that allows Shom to compile over 300 years of bathymetric knowledge. The Téthys project for Shom will be presented in detail. The implementation of this type of process is a scientific, algorithmic, and infrastructure challenge.

Evaluating the quality of data is a problem of a multi-dimensional nature and quite frequently depends on the perspective of an expected use or final purpose of the data. Numerous works have explored the well-known specification of data quality dimensions in various application domains, without addressing the inter-dependencies and aggregation of quality attributes for decision support. In this work we therefore propose a context-dependent formal process to evaluate the quality of data which integrates a preference model from the field of Multi-Criteria Decision Aiding. The parameters of this preference model are determined through interviews with work-domain experts. We show the interest of the proposal on a case study related to the evaluation of the quality of hydrographical survey data.

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.