Keywords
Awards & distinctions
- 2012 Lamba Mu of the Institute for Risk Management (IMDR)
Team
Conception de systèmes pour la sécurité et la sûreté de l’environnement marin
Biography
Aldo Napoli is a researcher whose work focuses primarily on the analysis and modeling of maritime data, particularly data from the Automatic Identification System (AIS). His research addresses critical issues such as the detection of anomalies in maritime traffic, the assessment of AIS signal coverage, and the integrity and reliability of data transmitted by these systems. He explores innovative methods—notably those based on artificial intelligence and graph neural networks (GNNs)—to improve the accuracy of signal reception models and optimize maritime surveillance. His contributions also include approaches to characterizing and quantifying port activity using AIS data, enabling a detailed analysis of economic and territorial dynamics. His work takes a multidisciplinary approach, combining geomatics, geography, and economics, while incorporating aspects related to cybersecurity and risk management in the maritime environment.
Publication(s)
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2025
Attention-Aware Graph Neural Network Modeling for AIS Reception Area Prediction DOI : 10.3390/s25196259
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2024
Port calls and vessel trajectory dataset in the Caribbean with accurate port quays survey DOI : 10.1016/j.dib.2024.110617
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2024
Port call extraction from vessel location data for characterising harbour traffic DOI : 10.1016/j.oceaneng.2024.116771
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2022
Predicting AIS reception using tropospheric propagation forecast and machine learning DOI : 10.23919/USNC-URSI52669.2022.9887465
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2021
Port Type Prediction Based on Machine Learning and AIS Data Analysis DOI : 10.23919/OCEANS44145.2021.9705864
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2021
Path planning for a maritime suface ship based on Deep Reinforcement Learning and weather data DOI : 10.23919/OCEANS44145.2021.9706088
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2020
An expert-based method for the risk assessment of anomalous maritime transportation data DOI : 10.1016/j.apor.2020.102337
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2020
Data integrity assessment for maritime anomaly detection DOI : 10.1016/j.eswa.2020.113219
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2019
Integrity and trust of geographic information DOI : 10.1002/9781119507284.ch04
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2019
Uses and Misuses of the Automatic Identification System DOI : 10.1109/OCEANSE.2019.8867559
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2017
An assessment approach of maritime supply chain of energy vulnerability to piracy risk by simulation of spatial behavior DOI : 10.1201/9781315374987-238
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2017
Risk analysis of falsified automatic identification system for the improvement of maritime traffic safety
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2016
A thorough analysis of the engineering solutions deployed to stop the oil spill following the deepwater horizon disaster DOI : 10.3303/CET1648130
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2016
A methodology to improve the assessment of vulnerability on the maritime supply chain of energy DOI : 10.23919/oceans.2015.7404414
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2016
Detection of false AIS messages for the improvement of maritime situational awareness DOI : 10.23919/oceans.2015.7401841
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2016
On the Interest of Data Mining for an Integrity Assessment of AIS Messages DOI : 10.1109/ICDMW.2016.0059
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2015
Data quality assessment for maritime situation awareness DOI : 10.5194/isprsannals-II-3-W5-291-2015
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2015
Modeling the dynamics of maritime territories to assess the vulnerability of the maritime net-work
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2015
DeAIS project: Detection of AIS spoofing and resulting risks DOI : 10.1109/OCEANS-Genova.2015.7271729
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2014
From Movement Data to Objects Behavior Using Semantic Trajectory and Semantic Events DOI : 10.1080/01490419.2014.902885
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2014
Using ontologies for proposing adequate geovisual analytics solutions in the analysis of trajectories DOI : 10.1109/IV.2014.26
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2014
A Bayesian network to manage risks of maritime piracy against offshore oil fields DOI : 10.1016/j.ssci.2014.04.010
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2012
The contribution of Bayesian networks to manage risks of maritime piracy against oil offshore fields DOI : 10.1007/978-3-642-29023-7_9
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2012
Application of Bayesian networks in planning a response to a pirate attack against an oil field; [Application des réseaux bayésiens à la planification de la réponse à une attaque de pirates contre un champ pétrolier]
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2012
The automatic identification system of maritime accident risk using rule-based reasoning DOI : 10.1109/SYSoSE.2012.6384140
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2012
Discovering association rules to aid in forecasting maritime accidents; [Découverte de règles d'association pour l'aide à la prévision des accidents maritimes]
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2012
Spatial ontologies for detecting abnormal maritime behaviour DOI : 10.1109/OCEANS-Yeosu.2012.6263532
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2012
Bayesian networks in the management of oil field piracy risk DOI : 10.2495/RISK120041
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2012
An enhanced spatial reasoning ontology for maritime anomaly detection DOI : 10.1109/SYSoSE.2012.6384120
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2012
Integration of a Bayesian network for response planning in a maritime piracy risk management system DOI : 10.1109/SYSoSE.2012.6384126
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2009
Toward a complete system for surveillance of the whole EEZ: ScanMaris and associated projects
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2008
Sense, enrich and classify: The scanmaris workshop for assessment of vessel's abnormal behavior in the EEZ DOI : 10.1109/OCEANS.2008.5151852
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2007
A geospatial web platform for natural hazard exposure assessment in the insurance sector DOI : 10.1007/978-1-84628-827-2_17
Projects
- 2024-2028 Interreg Maritime JASON Scientific Director JASON vise à capitaliser la recherche scientifique, les avancées technologiques et les résultats des projets précédemment mis en œuvre en matière de protection de l'environnement et de sécurité maritime dans la zone de coopération géographique couverte par le programme Interreg Maritime. À la lumière de ces travaux, JASON se concentrera sur trois questions spécifiques ayant un impact particulier sur l'environnement, ainsi que sur un sujet certain de réglementation et une source de demande de nouvelles performances opérationnelles, telles que les sources d'énergie alternatives (propulsives et non propulsives), les navires autonomes et la cybersécurité maritime et portuaire, toutes ayant pour horizon 2050.
PhD supervision
- 2021 Modeling AIS reception zones in maritime environments using graph neural networks RENAUD Ambroise
- 2019 Deep reinforcement learning in a cognitive architecture for naval mission assistance ARTUSI Eva