Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance

Abstract

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.

Online Long-Term Trajectory Prediction Based on Mined Route Patterns

“…We conducted experiments against real datasets (IFS messages and AIS messages [33]). Table 1 summarizes some basic statistics about the input dataset.…”

EvolvingClusters: Online Discovery of Group Patterns in Enriched Maritime Data

“…In our study, we use a publicly available maritime dataset, called Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance [22], which contains information on maritime traffic in France. The dataset ranges in time and space as follows: -temporal range: October 1st, 2015 to March 31st, 2016 (6 months); -spatial range: latitude in [45.00, 51.00], longitude in [-10.00, 0.00] (Celtic sea, the Channel and Bay of Biscay).…”

“…-We design and evaluate a unified group behaviour discovery algorithm able to simulate existing pattern discovery methods, such as flocks and convoys. -We evaluate the above over a large-volume real-world maritime trajectory dataset [22].…”

Composite Event Recognition for Maritime Monitoring

“…Our data comes from the datAcron project 3 . First, we used a publicly available stream of 18M AIS position signals, transmitted by 5K vessels sailing in the Atlantic Ocean around the port of Brest, France, between October 2015 and March 2016 [22]. Second, we employed a stream of 55M terrestrial and satellite AIS position signals transmitted by 34K vessels during January 2016 in the European seas [4].…”