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.

High resolution estimates of bottom towed fishing gears are needed to provide relevant information for natural resource management, impact assessment and maritime spatial planning. The use of satellite-based vessel monitoring system (VMS) data is constrained by data access restrictions as well as rather coarse data resolution. This study focuses on mapping dredge gear fishing grounds using fishing effort estimates at the métier level based on automatic identification system (AIS) data. The performance of the approach was evaluated in terms of correct discrimination between fishing and non-fishing activities for known fishing positions as well as appropriate error propagation. The test was conducted in the Bay of Brest (France) in partnership with a committee of local fishers. The results identified dredge fishing grounds for great scallop (Pecten maximus) in the western part of the Bay of Brest and provided high-resolution information for scientists and local decision makers on the spatial and temporal seasonal variability of fishing effort. The proposed method is semi-automatic and generic making it suitable for other applications.

Continuous location-dependent queries can be considered as key elements for the development of different categories of location-based and context-aware services. However, most work on location-dependent query processing has been mainly oriented towards outdoor environments. This paper studies location-dependent and context-aware queries over moving objects in indoor environments (e.g., houses, commercial malls, etc.), with a special focus on navigation-related queries (i.e., mainly path search and range queries). A hierarchical and context-dependent spatial data model is firstly presented, which leads to the consideration of other contextual dimensions besides the location of the involved entities, such as time and user profiles. Two algorithms for continuous processing of path and range queries on top of this modelling approach are introduced. The former performs a hierarchical and incremental path search for the continuous processing of path queries, and applied to both static and moving objects. The latter presents an incremental approach for continuous range queries, which performs a hierarchical network expansion around the initial query point and implements a mechanism to update the initial search tree based on user's movements.