@article {,
	title = {A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities},
	journal = {Sensors},
	volume = {21},
	year = {2021},
	month = {04/2021},
	abstract = {<p>Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight {\textquotedblleft}suspicious{\textquotedblright} AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel{\textemdash}and the gear it adopts{\textemdash}is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.</p>
},
	keywords = {Automatic Identification System, Machine Learning, maritime surveillance, Synthetic Aperture Radar data integration},
	issn = {1424-8220},
	doi = {10.3390/s21082756},
	url = {https://www.mdpi.com/1424-8220/21/8/2756},
	author = {Alessandro Galdelli and Adriano Mancini and Carmen Ferra Vega and Anna Nora Tassetti}
}
@conference {,
	title = {A Cloud Computing Architecture to Map Trawling Activities Using Positioning Data},
	year = {2019},
	abstract = {<p>Descriptive and spatially-explicit information on fisheries plays a key role for an efficient integrated management of the maritime activities and the sustainable use of marine resources. However, this information is today still hard to obtain and, consequently, is a major issue for implementing Marine Spatial Planning (MSP). Since 2002, the Automatic Identification System (AIS) has been undergoing a major development allowing now for a real time geo-tracking and identification of equipped vessels of more than 15m in length overall (LOA) and, if properly processed, for the production of adequate information for MSP. Such monitoring systems or other low-cost and low-burden solutions are still missing for small vessels (LOA \&lt; 12m), whose catches and fishing effort remain spatially unassessed and, hence, unregulated. In this context, we propose an architecture to process vessel tracking data, understand the behaviour of trawling fleets and map related fishing activities. It could be used to process not only AIS data but also positioning data from other low cost systems as IoT sensors that share their position over LoRa and 2G/3G/4G links. Analysis gives back important and verified data (overall accuracy of 92\% for trawlers) and opens up development perspectives for monitoring small scale fisheries, helping hence to fill fishery data gaps and obtain a clearer picture of the fishing grounds as a whole.</p>
},
	doi = {10.1115/DETC2019-97779},
	url = {https://doi.org/10.1115/DETC2019-97779},
	author = {Alessandro Galdelli and Adriano Mancini and Anna Nora Tassetti and Carmen Ferra Vega and Enrico Armelloni and Giuseppe Scarcella and Gianna Fabi and Primo Zingaretti}
}
