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.

Maritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We present such a system using the Run-Time Event Calculus, a composite event recognition system with formal, declarative semantics. For effective recognition, we developed a library of maritime patterns in close collaboration with domain experts. We present a thorough evaluation of the system and the patterns both in terms of predictive accuracy and computational efficiency, using real-world datasets of vessel position streams and contextual geographical information.

From the acoustic data acquired by the RHUM-RUM (Réunion Hotspot and Upper Mantle Réunions Unterer Mantel) Ocean Bottom Seismometer (OBS) network between October 2012 and November 2013, this study revealed baleen whale occurrence in the western Indian Ocean (IO). Low-frequency songs from three species (Antarctic Blue Whales, Pygmy Blue Whales and Fin Whales) as well as P-calls (or Spot-calls) from an unknown species were recorded on the dataset. The wide arrangement of the OBS network (2000 km × 2000 km) provided valuable information to draw seasonal patterns of occurrence and distribution all over the area. These species occurred sympatrically in the western IO, at least during austral autumn months emphasizing the importance of this region for these populations. This data set helped to refine the knowledge on their spatio-temporal distribution and complete the picture built by previous studies. A tighter sub-network of 8 OBSs deployed on the South West Indian Ridge provided ideal inter-sensor spacing for whale tracking. We demonstrated the capability of such array of detecting and tracking the three different whale species up to 50 km and for several hours. As a result and to understand the effect of acoustic wave propagation, songs from the tracking were described at a close and remote distance of the sensor. This work could also help to understand the local behavior of these species during austral autumn months in this area of the western Indian Ocean.

Abstract-As a first step to Antarctic Blue Whale monitoring, a new method based on a passive application of the Stochastic Matched Filter (SMF) is developed. To perform Z-call detection in noisy environment, improvements on the classical SMF requirements are proposed. The signal's reference is adjusted, the background noise estimation is reevaluated to avoid operator's selection, and the time-dependent Signal to Noise Ratio (SNR) estimation is revised by time-frequency analysis. To highlight the SMF's robustness against noise, it is applied on a Ocean Bottom Seismometers hydrophone-recorded data and compared to the classical Matched Filter: the output's SNR is maximized and the false alarm drastically decreased.

The Detection of Envelope Modulation on Noise (DEMON) is an algorithm that is commonly applied to hydrophone data for the detection and classification of underwater noise produced by a ship. This algorithm utilizes modulation analysis to determine the frequencies that modulate the broadband cavitation noise produced by marine vessel propellers. In this paper, a DEMON demodulator for acoustic vector sensors (AVSs) that are directional hydrophones capable of acquiring both the acoustic pressure and the components of the particle velocity vector is defined. The proposed method is able to extract multiple modulating signals and measure their direction of arrival. The proposed receiver was validated with real data collected at sea with a moving buoyancy glider hosting an AVS.