Michele Vespe

Mapping Fishing Effort through AIS Data

Several research initiatives have been undertaken to map fishing effort at high spatial resolution using the Vessel Monitoring System (VMS). An alternative to the VMS is represented by the Automatic Identification System (AIS), which in the EU became compulsory in May 2014 for all fishing vessels of length above 15 meters. The aim of this paper is to assess the uptake of the AIS in the EU fishing fleet and the feasibility of producing a map of fishing effort with high spatial and temporal resolution at European scale. After analysing a large AIS dataset for the period January-August 2014 and covering most of the EU waters, we show that AIS was adopted by around 75% of EU fishing vessels above 15 meters of length. Using the Swedish fleet as a case study, we developed a method to identify fishing activity based on the analysis of individual vessels’ speed profiles and produce a high resolution map of fishing effort based on AIS data. The method was validated using detailed logbook data and proved to be sufficiently accurate and computationally efficient to identify fishing grounds and effort in the case of trawlers, which represent the largest portion of the EU fishing fleet above 15 meters of length. Issues still to be addressed before extending the exercise to the entire EU fleet are the assessment of coverage levels of the AIS data for all EU waters and the identification of fishing activity in the case of vessels other than trawlers.

SAR Ship Detection and Self-Reporting Data Fusion Based on Traffic Knowledge

The improvement in Maritime Situational Awareness, the capability of understanding events, circumstances, and activities within and impacting the maritime environment, is nowadays of paramount importance for safety and security. The integration of spaceborne synthetic aperture radar (SAR) data and automatic identification system (AIS) information has the appealing potential to provide a better picture of what is happening at sea by detecting vessels that are not reporting their positioning data or, on the other side, by validating ships detected in satellite imagery. In this letter, we propose a novel architecture that is able to increase the quality of SAR/AIS fusion by exploiting knowledge of historical vessel positioning information. Experimental results are presented, testing the algorithm in the specific area of Dover Strait using real SAR and AIS data.

Maritime awareness for counter-piracy in the Gulf of Aden

Maritime awareness is a keystone of counter-piracy activities, as they are nowadays unfortunately called for in the Gulf of Aden and the Western Indian Ocean. There are a number of space-based systems that can be used to obtain knowledge of shipping and ship traffic patterns beyond coastal range, e.g. Satellite AIS, LRIT and satellite SAR. Based on data gathered during a trial in 2010, this paper analyses the capabilities of these systems when used to obtain a fused maritime picture. It is concluded that all data sources contribute to the maritime picture, but that in particular for Satellite AIS the update rates need to increase to enable accurate fusion of the non-cooperative data.

Mapping EU fishing activities using ship tracking data

ABSTRACT Information and understanding of fishing activities at sea are fundamental components of marine knowledge and maritime situational awareness. Such information is important to fisheries science, public authorities and policy-makers. In this paper we introduce a first map at European scale of EU fishing activities extracted using Automatic Identification System ship tracking data. The resulting map is a density of points that identify fishing activities. A measure of the reliability of such information is also presented as a map of coverage reception capabilities.

Oil spill detection using COSMO-SkyMed over the adriatic sea: The operational potential

In this paper the potential of COSMO-SkyMed is examined for oil spill detection, focusing on the Adriatic Sea. The low revisit time and the radiometric characteristics of COSMO-SkyMed point to good suitability of the instrument for operational oil spill detection in the area. The possibility of querying Automatic Identification System (AIS) data over the area, with almost no coverage gaps, offers a unique instrument to aid verification activities and the eventual identification of the discharging ship.

A novel anomaly detection approach to identify intentional AIS on-off switching

An anomaly detection algorithm to identify AIS on-off switching is proposed.The algorithm exploits the AIS message Received Signal Strength Indicator.Machine Learning algorithms are used to build normality models.AIS reception is characterized by using real word data.The methodology is scalable from one station to a network of receivers. The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset.

Knowledge Discovery of Human Activities at Sea in the Arctic Using Remote Sensing and Vessel Tracking Systems

Adequate knowledge of human activities in the Arctic is fundamental to support safe and secure maritime operations and sustainable development in the area. Such knowledge is often incomplete in terms of activities, geographic area and spatial resolution. For example, in the specific case of the transits over the Arctic shipping routes, such information can be accessed through domain expert knowledge, open source statistics or data from ship reporting systems. Offshore energy and exploration, fishing, and shipping activities can be monitored and/or mapped using surveillance tools such as satellite based remote sensing (e.g. Synthetic Aperture Radar—SAR) and vessel tracking systems (e.g. Automatic Identification Systems—AIS, and Long Range Identification and Tracking—LRIT), supplemented by knowledge discovery approaches. Such data-driven methodology, combined with meteorological and oceanographic information, enables a high level of situational awareness that is otherwise often difficult to access, hard to update or challenging to extract. In this chapter we analyse ways to understand and characterise activities and discover their trends in the Arctic. This new information will assist policy makers and operational authorities when conducting Maritime Spatial Planning and the evaluation of new routing systems and impact assessments of Marine Protected Areas.

Perspectives on Oil Spill Detection using Synthetic Aperture Radar

Deliberate illegal discharges of oil at sea can be reduced by law enforcement and via improving the monitoring and controlling capabilities. During the last decades, satellite based Synthetic Aperture Radar (SAR) images have been consistently and progressively used to achieve global monitoring of the seas. Satellite based oil spill detection is nowadays an operational service in Europe (CleanSeaNet run by EMSA), placing increasingly strict requirements in terms of detection performance, i.e. false alarms and missed detections mitigation. In this work, an overview of oil spill detection from satellite based SAR is given, with particular attention to the recent advances and trends in optimising and enhancing these achievements for operational use. Confidence level, ancillary data, data fusion and SAR data quality assessment are discussed together with their impact on the final “reliability” of the oil spill monitoring application.

WiFi positioning and Big Data to monitor flows of people on a wide scale

The possibility to count the accesses to a site and monitor the internal movements of people can be useful in many different scenarios. In this respect, a WiFi network can be exploited to count accesses and estimate users position. This study extends this principle to a wide spatial area and to a large number of users, introducing synergies between Big Data and localization techniques. The 2016 Open Day of the Joint Research Centre (JRC), Ispra (Italy), was a good opportunity to investigate the potential of Big Data and positioning techniques. During the event, which counted the participation of some 8000 people within an area of about 167 hectares, 20 WiFi access points, scattered across the site, recorded the access of wireless devices, such as smartphones and tablets, belonging to visitors and volunteers. By exploiting the Media Access Control (MAC) address (the device unique identifier) through a data-cleaning process, the data analysis allowed estimating the number of participants to the event and the space/time evolution of their position. Moreover, the visitors flow was reconstructed using a Weigthed Centroid (WeC) algorithm. The results achieved, in terms of number of participants, confirmed the data of the JRC registry report compiled at the entrance points of the area. In addition, the results relative to the people flow within the site were found compatible with the scheduling of the event and with its actual progress.

Maritime Traffic Networks: From Historical Positioning Data to Unsupervised Maritime Traffic Monitoring

The large maritime traffic volume and its implications in economy, environment, safety, and security require an unsupervised system to monitor maritime traffic. In this paper, a method is proposed to automatically produce synthetic maritime traffic representations from historical self-reporting positioning data, more specifically from automatic identification system data. The method builds a two-layer network that represents the maritime traffic in the monitored area, where the external layer presents the network’s basic structure and the inner layer provides precision and granularity to the representation. The method is tested in a specific scenario with high traffic density, the Baltic Sea. Experimental results reveal a decrease of over 99% storage data with a negligible precision drop. Finally, the novel method presents a light and structured representation of the maritime traffic, which sets the foundations to real-time automatic maritime traffic monitoring, anomaly detection, and situation prediction.