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Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance

Facing an ever-increasing amount of traffic at sea, many research centres, international organisations, and industrials have favoured and developed sensors together with detection techniques for the monitoring, analysis, and visualisation of sea movements. The Automatic Identification System (AIS) i...

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Autores principales: Ray, Cyril, Dréo, Richard, Camossi, Elena, Jousselme, Anne-Laure, Iphar, Clément
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612624/
https://www.ncbi.nlm.nih.gov/pubmed/31321262
http://dx.doi.org/10.1016/j.dib.2019.104141
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author Ray, Cyril
Dréo, Richard
Camossi, Elena
Jousselme, Anne-Laure
Iphar, Clément
author_facet Ray, Cyril
Dréo, Richard
Camossi, Elena
Jousselme, Anne-Laure
Iphar, Clément
author_sort Ray, Cyril
collection PubMed
description Facing an ever-increasing amount of traffic at sea, many research centres, international organisations, and industrials have favoured and developed sensors together with detection techniques for the monitoring, analysis, and visualisation of sea movements. The Automatic Identification System (AIS) is one of the electronic systems that enable ships to broadcast their position and nominative information via radio communication. In addition to these systems, the understanding of maritime activities and their impact on the environment also requires contextual maritime data capturing additional features to ships' kinematic from complementary data sources (environmental, contextual, geographical, …). The dataset described in this paper contains ship information collected through the AIS, prepared together with spatially and temporally correlated data characterising the vessels, the area where they navigate and the situation at sea. The dataset contains four categories of data: navigation data, vessel-oriented data, geographic data, and environmental data. It covers a time span of six months, from October 1st, 2015 to March 31st, 2016 and provides ship positions over the Celtic sea, the North Atlantic Ocean, the English Channel, and the Bay of Biscay (France). The dataset is proposed for an easy integration with relational databases. This relies on the widespread and open source relational database management system PostgreSQL, with the adjunction of the geospatial extension PostGIS for the treatment of all spatial features of the dataset.
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spelling pubmed-66126242019-07-18 Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance Ray, Cyril Dréo, Richard Camossi, Elena Jousselme, Anne-Laure Iphar, Clément Data Brief Computer Science Facing an ever-increasing amount of traffic at sea, many research centres, international organisations, and industrials have favoured and developed sensors together with detection techniques for the monitoring, analysis, and visualisation of sea movements. The Automatic Identification System (AIS) is one of the electronic systems that enable ships to broadcast their position and nominative information via radio communication. In addition to these systems, the understanding of maritime activities and their impact on the environment also requires contextual maritime data capturing additional features to ships' kinematic from complementary data sources (environmental, contextual, geographical, …). The dataset described in this paper contains ship information collected through the AIS, prepared together with spatially and temporally correlated data characterising the vessels, the area where they navigate and the situation at sea. The dataset contains four categories of data: navigation data, vessel-oriented data, geographic data, and environmental data. It covers a time span of six months, from October 1st, 2015 to March 31st, 2016 and provides ship positions over the Celtic sea, the North Atlantic Ocean, the English Channel, and the Bay of Biscay (France). The dataset is proposed for an easy integration with relational databases. This relies on the widespread and open source relational database management system PostgreSQL, with the adjunction of the geospatial extension PostGIS for the treatment of all spatial features of the dataset. Elsevier 2019-06-11 /pmc/articles/PMC6612624/ /pubmed/31321262 http://dx.doi.org/10.1016/j.dib.2019.104141 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Computer Science
Ray, Cyril
Dréo, Richard
Camossi, Elena
Jousselme, Anne-Laure
Iphar, Clément
Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance
title Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance
title_full Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance
title_fullStr Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance
title_full_unstemmed Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance
title_short Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance
title_sort heterogeneous integrated dataset for maritime intelligence, surveillance, and reconnaissance
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612624/
https://www.ncbi.nlm.nih.gov/pubmed/31321262
http://dx.doi.org/10.1016/j.dib.2019.104141
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