@proceedings {,
	title = {A low-cost and low-burden secure solution to track small-scale fisheries},
	journal = {Conference: 2021 International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea)},
	year = {2021},
	month = {11/2021},
	pages = {382-387},
	abstract = {<p>During the last decade accurate spatial and quantitative information of industrial fisheries have been increasingly given using tracking technologies and machine learning analytical algorithms. However, in most small-scale fisheries, lack of spatial data has been a recurrent bottleneck as Vessel Monitoring System and Automatic Identification System, developed for vessels longer than 12 and 15 m in length respectively, have little applicability in these contexts. It follows that small-scale vessels (\&lt; 12 m in length) remain untracked and largely unregulated, even though they account for most of the fishing fleet in operation in the Mediterranean Sea. As such, the tracking of small-scale fleets tends to require the use of novel and low cost solutions that could be addressed by small vessels often without dedicated electrical systems. In this paper we propose a scalable architecture that makes use of a low-cost LoRaWAN/cellular network to acquire and process positioning data from small-scale vessels; preliminary results of a first installation of the prototype are presented, as well as the data collected. The emergence of a such low-cost and open source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management, and cross-border marine spatial planning.</p>
},
	keywords = {cloud computing, fleet management system, maritime communication, small-scale fisheries, vessel position data},
	doi = {https://doi.org/10.1109/MetroSea52177.2021.9611622},
	url = {https://ieeexplore.ieee.org/document/9611622},
	author = {Anna Nora Tassetti and Alessandro Galdelli and Jacopo Pulcinella}
}
@conference {Malinverni20102836,
	title = {LCLU Information System for object-oriented nomenclature},
	booktitle = {International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2010},
	note = {cited By 0},
	pages = {2836-2839},
	abstract = {A Land Cover/Land Use (LCLU) Information System is proposed as a new dynamic and flexible approach to describe landscape objects. It is able to give a deeper and more realistic thematic description by storing membership land cover attributes for each polygon automatically extracted and classified by the T-MAP software. The proposed approach can overcome the traditional "hard" classification by taking directly into account "fuzzy" cover components and making the classification approach more bounded with the polygon characteristics and their changes. The LCLU Information System can be easily integrated with different databases, making it suitable for different nomenclatures and further analysis, regarding environmental indexes, class updating and classification stability assessment. {\textcopyright} 2010 IEEE.},
	doi = {10.1109/IGARSS.2010.5651398},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78650884639\&partnerID=40\&md5=a38544ae56c5f11edee2b11e7730439c},
	author = {Eva Savina Malinverni and Anna Nora Tassetti and Primo Zingaretti}
}
