@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 = {

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 (\< 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.

}, 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} } @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 = {

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.

}, 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 = {

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 \< 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.

}, 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} } @article {sturari2017integrating, title = {Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping}, journal = {European Journal of Remote Sensing}, volume = {50}, number = {1}, year = {2017}, pages = {1{\textendash}17}, publisher = {Taylor \& Francis}, author = {Mirco Sturari and Emanuele Frontoni and Roberto Pierdicca and Adriano Mancini and Eva Savina Malinverni and Anna Nora Tassetti and Primo Zingaretti} } @article {pierdicca2016smart, title = {Smart maintenance of riverbanks using a standard data layer and Augmented Reality}, journal = {Computers \& Geosciences}, volume = {95}, year = {2016}, pages = {67{\textendash}74}, publisher = {Pergamon}, author = {Roberto Pierdicca and Emanuele Frontoni and Primo Zingaretti and Adriano Mancini and Eva Savina Malinverni and Anna Nora Tassetti and Marcheggiani, Ernesto and Galli, Andrea} } @article {Mancini2013409, title = {A novel method for fast processing of large remote sensed image}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8157 LNCS}, number = {PART 2}, year = {2013}, note = {cited By 1}, pages = {409-418}, abstract = {In this paper we present a novel approach to reduce the computational load of a CFAR detector. The proposed approach is based on the use of integral images to directly manage the presence of masked pixels or invalid data and reduce the computational time. The approach goes through the challenging problem of ship detection from remote sensed data. The capability of fast image processing allows to monitor the marine traffic and identify possible threats. The approach allows to significantly boost the performance up to 50x working with very high resolution image and large kernels. {\textcopyright} 2013 Springer-Verlag.}, doi = {10.1007/978-3-642-41184-7_42}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84884709393\&partnerID=40\&md5=335f37537f59723ffcfa04255b21821b}, author = {Adriano Mancini and Anna Nora Tassetti and Cinnirella, A. and Emanuele Frontoni and Primo Zingaretti} } @article {Malinverni20111025, title = {Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery}, journal = {International Journal of Geographical Information Science}, volume = {25}, number = {6}, year = {2011}, note = {cited By 13}, pages = {1025-1043}, abstract = {Traditionally, remote sensing has employed pixel-based classification techniques to deal with land use/land cover (LULC) studies. Generally, pixel-based approaches have been proven to work well with low spatial resolution imagery (e.g. Landsat or System Pour L{\textquoteright}Observation de la Terre sensors). Now, however, commercially available high spatial resolution images (e.g. aerial Leica ADS40 and Vexcel UltraCam sensors, and satellite IKONOS, Quickbird, GeoEye and WorldView sensors) can be problematic for pixel-based analysis due to their tendency to oversample the scene. This is driving research towards object-based approaches. This article proposes a hybrid classification method with the aim of incorporating the advantages of supervised pixel-based classification into object-based approaches. The method has been developed for medium- scale (1:10,000) LULC mapping using ADS40 imagery with 1 m ground sampling distance. First, spatial information is incorporated into a pixel-based classification (AdaBoost classifier) by means of additional texture features (Haralick, Gabor, Law features), which can be selected {\textquoteright}ad hoc{\textquoteright} according to optimal training samples ({\textquoteright}Relief-F{\textquoteright} pproach,Mahalanobis distances). Then a rule-based approach sorts segmented regions into thematic CORINE Land Cover classes in terms of membership class percentages (a modified Winner-Takes-All approach) and shape parameters. Finally, ancillary data (roads, rivers, etc.) are exploited to increase classification accuracy. The experimental results show that the proposed hybrid approach allows the extraction of more LULC classes than conventional pixel-based methods, while improving classification accuracy considerably. A second contribution of this article is the assessment of classification reliability by implementing a stability map, in addition to confusion matrices. {\textcopyright} 2011 Taylor \& Francis.}, doi = {10.1080/13658816.2011.566569}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960685342\&partnerID=40\&md5=18767c8a88bf2abff53ef96ec138f0aa}, author = {Eva Savina Malinverni and Anna Nora Tassetti and Adriano Mancini and Primo Zingaretti and Emanuele Frontoni and A. Bernardini} } @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} } @article {Bernardini201043, title = {Pixel, object and hybrid classification comparisons}, journal = {Journal of Spatial Science}, volume = {55}, number = {1}, year = {2010}, note = {cited By 3}, pages = {43-54}, abstract = {The choice of the best classification approach for thematic map generation relies on many factors, such as image resolution and minimum mapping unit. The generalized GIS-ready products derived from the results of pixel-based approaches and the availability of higherresolution imagery have directed research towards object-based classification approaches. In this paper we present the superior performance of a hybrid methodology that combines the results of automatic segmentation with the land cover information derived from a pixel classification by means of the Winner Takes All (WTA) algorithm. Land use and land cover results obtained through this hybrid classification approach are compared with those of a One Against All (OAA) object-oriented classification approach. {\textcopyright} 2010 Surveying and Spatial Sciences Institute and Mapping Sciences Institute, Australia.}, doi = {10.1080/14498596.2010.487641}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80052137947\&partnerID=40\&md5=74b7a4fe82987b5213c27b7315b0f768}, author = {A. Bernardini and Emanuele Frontoni and Eva Savina Malinverni and Adriano Mancini and Anna Nora Tassetti and Primo Zingaretti} }