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- 88fba8bd-f2f0-402e-8147-b73b71e8691a description "Facing an increasing amount of movements at sea and daily impacts on ships, crew and our global ecosystem, many research centers, international organizations, industrials have favored and developed sensors, detection techniques for the monitoring, analysis and visualization of sea movements. Automatic Identification System (AIS) is one of these electronic systems that enable ships to broadcast their dynamic (position, speed, destination...) and static (name, type, international identifier…) information via radio communications. Having spatially and temporally aligned maritime dataset relying not only on ships' positions but also on a variety of complementary data sources is of great interest for the understanding of maritime activities and their impact on the environment. This dataset contains ships' information collected though the Automatic Identification System, integrated with a set of complementary data having spatial and temporal dimensions aligned. 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 ships positions within Celtic sea, the Channel and Bay of Biscay (France). The dataset is proposed with predefined integration and querying principles for relational databases. These rely on the widespread and free relational database management system PostgreSQL, with the adjunction of the PostGIS extension, for the treatment of all spatial features proposed in the dataset." assertion.
- j.dib.2019.104141 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." assertion.
- 3003421.3003423 description "The main objectives of moving objects queries are to search for objects that either lie in some specific areas (i.e., range queries) or are close to one specific location (i.e., kNN queries). Such queries have been previously studied considering either offline database processes using some index techniques or online approaches where incoming data are processed to answer those queries "on the fly". The research presented in this paper considers hybrid queries applied to historical data as well as streaming data. When considering the specific context of the maritime domain and moving objects at sea, a key issue is to make a difference between covered and non covered areas (i.e., regions from where AIS positioning signals are either received or not received). This leads us to introduce the concept of "Black Holes" query where the objective is to identify regions respectively covered and non covered, this providing useful insights for maritime authorities in charge of the regulation of maritime transportation." assertion.
- zenodo.1167595 description "This dataset contains ships' information collected though the Automatic Identification System, integrated with a set of complementary data having spatial and temporal dimensions aligned. 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 ships positions within Celtic sea, the Channel and Bay of Biscay (France). The dataset is proposed with predefined integration and querying principles for relational databases. These rely on the widespread and free relational database management system PostgreSQL, with the adjunction of the PostGIS extension, for the treatment of all spatial features proposed in the dataset." assertion.
- zenodo.1182538 description "For an efficient and advanced spatio-temporal analysis, reported information of vessels locations and behaviours must be cross checked with others and cartographical data. Amongst cartographical information, the International Hydrographic Organisation (IHO) defined a vector interchange format used for maritime charts. From this format (S-57 or its revision S-100) ensues the ENC (Electronic Nautical Chart) product specification use in electronic chart display and traffic control visualisation. It provides a data structure and format that are used to implement a data model and characterises spatial entities and geometrical primitives. The dataset labelled 10.5281/zenodo.1167595 contains ships' information collected though the Automatic Identification System (AIS), prepared together with a set of related data having spatial and temporal dimensions aligned. This technical note provides additional information for the integration of S57 nautical charts in this dataset." assertion.
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- 88fba8bd-f2f0-402e-8147-b73b71e8691a cite-as "Cyril Ray, Richard DRÉO, Richard DRÉO, Elena Camossi, and Anne-Laure Jousselme. "Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance." ROHub. Jan 10 ,2023. https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a." assertion.
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