@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} } @conference {, title = {A Synergic Photometric Stereo and Super Resolution Approach for Optical Inspection}, booktitle = {2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)}, year = {2018}, month = {2018}, pages = {1-8}, abstract = {

Three-dimensional reconstruction is a very important technique of Computer Vision that produces a three-dimensional (3D) model of a real scene. For this purpose, we use Photometric Stereo that allows you to shape estimation from several images under different lighting conditions. In the last few years, this technology has been used in many areas both working and scientific. An important sector where Photometric Stereo is used is quality control inside productive cycle. In some case, the model 3D is used to identify defects or to make measurements of the order of hundredths or even thousandths of millimeters. To obtain an accurate 3D model, it is necessary to use an cost-effective reconstruction system with a performance hardware. Alternatively, we propose a method that combine Photometric Stereo with Super Resolution that produces a very accurate model using cheap and lower performance hardware that is suitable for optical inspection. Indeed, the Super Resolution allows to overcome the hardware constraint and produce one high resolution image from a set of low resolution images. With this novel method, we obtain a very accurate 3D model that could be in a decision support system for optical inspection.

}, doi = {10.1109/MESA.2018.8449206}, url = {https://doi.org/10.1109/MESA.2018.8449206}, author = {Alessandro Galdelli and Adriano Mancini and Emanuele Frontoni and Primo Zingaretti} }