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This documentation can be useful to understand how AIS data can be processed. https://api.vtexplorer.com/docs/response-ais.html http://schema.org/encodingFormat text/html https://api.vtexplorer.com/docs/response-ais.html http://schema.org/keywords API https://api.vtexplorer.com/docs/response-ais.html http://schema.org/license https://choosealicense.com/no-permission/ https://api.vtexplorer.com/docs/response-ais.html http://schema.org/name VTexplorer API documentation. https://api.vtexplorer.com/docs/response-ais.html http://schema.org/sdDatePublished 2022-12-08 11:55:50.363662+00:00 https://api.vtexplorer.com/docs/response-ais.html http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://api.vtexplorer.com/docs/response-ais.html http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://api.vtexplorer.com/docs/response-ais.html http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/SoftwareDocumentation https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/contentUrl https://celestrak.org/NORAD/documentation/tle-fmt.php https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/dateCreated 2022-12-24 09:43:33.106536+00:00 https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/dateModified 2022-12-24 09:43:33.827515+00:00 https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/description This document describes the NORA Two-Line Element Set Format (TLE) where data for each satellite consists of three lines with a fixed format (see document). https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/keywords TLE https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/license https://choosealicense.com/no-permission/ https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/name NORAD Two-Line Element Set Format https://celestrak.org/NORAD/documentation/tle-fmt.php http://schema.org/sdDatePublished 2022-12-24 09:43:33.106536+00:00 https://celestrak.org/NORAD/documentation/tle-fmt.php http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Document https://celestrak.org/NORAD/documentation/tle-fmt.php http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://celestrak.org/NORAD/documentation/tle-fmt.php http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://schema.org/contentUrl https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://schema.org/dateCreated 2022-12-03 12:22:11.080763+00:00 https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://schema.org/dateModified 2022-12-03 12:22:11.948165+00:00 https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://schema.org/description Main document (Google doc) provided when willing to start with TSAR Overview. https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://schema.org/license https://choosealicense.com/no-permission/ https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://schema.org/name TSAR Overview https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://schema.org/sdDatePublished 2022-12-03 12:22:11.080763+00:00 https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Document https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://doi.org/10.3390/jmse10010112 http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://doi.org/10.3390/jmse10010112 http://schema.org/contentUrl https://doi.org/10.3390/jmse10010112 https://doi.org/10.3390/jmse10010112 http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://doi.org/10.3390/jmse10010112 http://schema.org/dateCreated 2022-10-19 12:26:00.283770+00:00 https://doi.org/10.3390/jmse10010112 http://schema.org/dateModified 2022-12-19 18:00:27.575120+00:00 https://doi.org/10.3390/jmse10010112 http://schema.org/description The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations. https://doi.org/10.3390/jmse10010112 http://schema.org/keywords AIS https://doi.org/10.3390/jmse10010112 http://schema.org/keywords anomaly detection https://doi.org/10.3390/jmse10010112 http://schema.org/keywords automatic identification system https://doi.org/10.3390/jmse10010112 http://schema.org/keywords maritime safety https://doi.org/10.3390/jmse10010112 http://schema.org/keywords maritime security https://doi.org/10.3390/jmse10010112 http://schema.org/license https://choosealicense.com/no-permission/ https://doi.org/10.3390/jmse10010112 http://schema.org/name Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches https://doi.org/10.3390/jmse10010112 http://schema.org/sdDatePublished 2022-10-19 12:26:00.283770+00:00 https://doi.org/10.3390/jmse10010112 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roterms#Paper https://doi.org/10.3390/jmse10010112 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://doi.org/10.3390/jmse10010112 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/contentUrl https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/dateCreated 2022-10-05 13:34:23.536054+00:00 https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/dateModified 2022-12-13 15:27:48.034278+00:00 https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/description Internally shared google drive with data and documents for T-SAR project https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/keywords folder https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/license https://choosealicense.com/no-permission/ https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/name TSAR google drive project area https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://schema.org/sdDatePublished 2022-10-05 13:34:23.536054+00:00 https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Thing https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/contentUrl https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/dateCreated 2022-12-19 17:55:01.781065+00:00 https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/dateModified 2022-12-19 18:00:49.447899+00:00 https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/description Slides (private) presenting the T-SAR project. https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/keywords AIS https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/keywords Vessel https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/license https://choosealicense.com/no-permission/ https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/name TSAR project overview https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://schema.org/sdDatePublished 2022-12-19 17:55:01.781065+00:00 https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/PresentationDigitalDocument https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://schema.org/contentUrl https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://schema.org/dateCreated 2022-12-19 19:08:04.054398+00:00 https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://schema.org/dateModified 2022-12-19 19:08:05.371245+00:00 https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://schema.org/description In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations. https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://schema.org/license https://choosealicense.com/no-permission/ https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://schema.org/name Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://schema.org/sdDatePublished 2022-12-19 19:08:04.054398+00:00 https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roterms#Paper https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/contentUrl https://en.wikipedia.org/wiki/Two-line_element_set https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/dateCreated 2022-12-26 10:22:06.052178+00:00 https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/dateModified 2022-12-26 10:22:07.502404+00:00 https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/description Description of Two-Line Element Set (TLE) from Wikipedia. https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/keywords wikipedia https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/license https://choosealicense.com/no-permission/ https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/name Two-Line Element Set (wikipedia) https://en.wikipedia.org/wiki/Two-line_element_set http://schema.org/sdDatePublished 2022-12-26 10:22:06.052178+00:00 https://en.wikipedia.org/wiki/Two-line_element_set http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Document https://en.wikipedia.org/wiki/Two-line_element_set http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://en.wikipedia.org/wiki/Two-line_element_set http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/contentUrl https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/dateCreated 2023-01-05 14:54:54.005940+00:00 https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/dateModified 2023-01-05 14:54:57.935510+00:00 https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/description Gitlab repository set up for reproducibility purposes. https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/keywords gitlab https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/license https://choosealicense.com/no-permission/ https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/name repo set up for reproducibility purposes (private gitlab) https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ http://schema.org/sdDatePublished 2023-01-05 14:54:54.005940+00:00 https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/ 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https://gitlab.com/simula_ais_message/marivisu-v2 http://schema.org/dateModified 2022-10-05 13:37:32.047690+00:00 https://gitlab.com/simula_ais_message/marivisu-v2 http://schema.org/description Marivisu serves as a demonstrator of the machine learning model developed to detect anomalies in the vessel trajectory. This work was supported by the Norwegian Research Council (RCN) TSAR project under contract 287893. Satellite AIS data used for model development and testing has been made available courteously by its owner, the Norwegian Coastal Administration (Kystverket). https://gitlab.com/simula_ais_message/marivisu-v2 http://schema.org/license https://choosealicense.com/no-permission/ https://gitlab.com/simula_ais_message/marivisu-v2 http://schema.org/name Marivisu v2 https://gitlab.com/simula_ais_message/marivisu-v2 http://schema.org/sdDatePublished 2022-10-05 13:37:31.278559+00:00 https://gitlab.com/simula_ais_message/marivisu-v2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://gitlab.com/simula_ais_message/marivisu-v2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://gitlab.com/simula_ais_message/marivisu-v2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://w3id.org/ro/earth-science#SWDocumentation https://gitlab.com/simula_ais_message/pre-processing http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://gitlab.com/simula_ais_message/pre-processing http://schema.org/contentUrl https://gitlab.com/simula_ais_message/pre-processing https://gitlab.com/simula_ais_message/pre-processing http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://gitlab.com/simula_ais_message/pre-processing http://schema.org/dateCreated 2022-10-05 13:38:39.041164+00:00 https://gitlab.com/simula_ais_message/pre-processing http://schema.org/dateModified 2022-10-05 13:38:39.630828+00:00 https://gitlab.com/simula_ais_message/pre-processing http://schema.org/description n maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transhipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) messages transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations. https://gitlab.com/simula_ais_message/pre-processing http://schema.org/license https://choosealicense.com/no-permission/ https://gitlab.com/simula_ais_message/pre-processing http://schema.org/name Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning https://gitlab.com/simula_ais_message/pre-processing http://schema.org/sdDatePublished 2022-10-05 13:38:39.041164+00:00 https://gitlab.com/simula_ais_message/pre-processing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://gitlab.com/simula_ais_message/pre-processing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://gitlab.com/simula_ais_message/pre-processing http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://w3id.org/ro/earth-science#SWDocumentation https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://schema.org/contentUrl https://gitlab.com/simula_ais_message/vesseltype_identification_dae https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://schema.org/dateCreated 2022-11-15 13:19:46.330017+00:00 https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://schema.org/dateModified 2022-11-15 13:19:53.225356+00:00 https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://schema.org/description Private repository containing Anomaly Detection in Vessels Trajectories using Context-Aware Autoencoders https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://schema.org/license https://choosealicense.com/no-permission/ https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://schema.org/name vesseltype_identification_dae (gitlab private repo) https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://schema.org/sdDatePublished 2022-11-15 13:19:46.330017+00:00 https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://gitlab.com/simula_ais_message/vesseltype_identification_dae http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#SWDocumentation https://hackmd.io/@simula/tsar-project http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://hackmd.io/@simula/tsar-project http://schema.org/contentUrl https://hackmd.io/@simula/tsar-project https://hackmd.io/@simula/tsar-project http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://hackmd.io/@simula/tsar-project http://schema.org/dateCreated 2022-10-06 08:13:34.706608+00:00 https://hackmd.io/@simula/tsar-project http://schema.org/dateModified 2023-01-18 15:58:48.133720+00:00 https://hackmd.io/@simula/tsar-project http://schema.org/description Here we gather information about the project (notes taken during meetings, etc.). We use hackmd.io and text is written in markdown. https://hackmd.io/@simula/tsar-project http://schema.org/keywords folder https://hackmd.io/@simula/tsar-project http://schema.org/license https://choosealicense.com/no-permission/ https://hackmd.io/@simula/tsar-project http://schema.org/name TSAR meeting notes (hackmd) https://hackmd.io/@simula/tsar-project http://schema.org/sdDatePublished 2022-10-06 08:13:34.706608+00:00 https://hackmd.io/@simula/tsar-project http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://hackmd.io/@simula/tsar-project http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/HowTo https://hackmd.io/@simula/tsar-project http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://orcid.org/0000-0001-6489-8858 http://schema.org/affiliation Simula Research Laboratory https://orcid.org/0000-0001-6489-8858 http://schema.org/email dokken@simula.no https://orcid.org/0000-0001-6489-8858 http://schema.org/name Jørgen Schartum Dokken https://orcid.org/0000-0001-6489-8858 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://xmlns.com/foaf/0.1/Agent https://orcid.org/0000-0001-6489-8858 https://w3id.org/dingo#orcid 0000-0001-6489-8858 https://orcid.org/0000-0002-1784-2920 http://schema.org/affiliation Simula Research Laboratory https://orcid.org/0000-0002-1784-2920 http://schema.org/email annef@simula.no https://orcid.org/0000-0002-1784-2920 http://schema.org/name Anne Fouilloux https://orcid.org/0000-0002-1784-2920 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://xmlns.com/foaf/0.1/Agent https://orcid.org/0000-0002-1784-2920 https://w3id.org/dingo#orcid 0000-0002-1784-2920 https://orcid.org/0000-0002-7715-7052 http://schema.org/affiliation Simula Research Laboratory https://orcid.org/0000-0002-7715-7052 http://schema.org/email roehr@simula.no https://orcid.org/0000-0002-7715-7052 http://schema.org/name Thomas Roehr https://orcid.org/0000-0002-7715-7052 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https://orcid.org/0000-0002-1784-2920 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/author https://orcid.org/0000-0002-7715-7052 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/author mailto:pierbernabe@simula.no https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/contentSize 6363978 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/contentUrl https://api.rohub.org/api/ros/7998d851-41e8-4c51-aa06-deff6fd5f09a/crate/download/ https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/contributor mailto:dusica@simula.no https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/copyrightHolder https://ror.org/00vn06n10 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/creator mailto:annefou@geo.uio.no https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/dateCreated 2022-10-04 13:39:13.980365+00:00 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/dateModified 2025-10-18 11:20:47.365546+00:00 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/datePublished 2022-10-04 13:39:13.980365+00:00 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/description In transport infrastructures, vessel traffic services, air traffic management, and connected cars all rely on unauthenticated and unencrypted messages transfer that renders these services vulnerable to cyberattacks. Typical attacks such as False Data Injection Attacks (FDIA) are challenging to detect as they alter the semantics of the data (e.g., by adding/removing/multiplying elements on real-time control equipment), while preserving the syntactical correctness of the messages. Identifying these attacks and classifying them as serious threats or unintentional false data has become a significant challenge of traffic monitoring authorities. The TSAR project aims at demonstrating that recent advances in Artificial Intelligence (AI) can be leveraged in the automatic detection of FDIA in transport infrastructures. By combining realistic threat data generation based on constraint-based software testing techniques and automatic detection with deep reinforcement learning, TSAR will propose a new technology for automatic FDIA generation and detection. This technology will be empirically evaluated with end-users from the maritime domain and with open and accessible data in two other domains, namely air traffic control, and connected cars. By leveraging automatic detection of FDIA in traffic management systems, TSAR will also prepare the ground for the upcoming revolution in traffic management, which concerns, self-driving vessels, self-driving aircraft, and self-driving cars. https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/encodingFormat application/ld+json https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/hasPart https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/folders/0bdb922b-ce4e-4dda-af06-9182927662b9 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/hasPart https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/folders/4c580e28-b6c6-4c39-a0fa-95a016f28fc4 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ http://schema.org/hasPart https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/folders/706b8928-34f0-476a-93e4-b9bdcc97fa34 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ 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https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://schema.org/dateCreated 2022-10-17 19:53:16.725831+00:00 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://schema.org/dateModified 2022-10-17 19:53:19.988447+00:00 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://schema.org/description The NorSat-3 microsatellite will be launched into space during spring 2021 with a radar detector developed at the Norwegian Defence Research Establishment (FFI). It will provide improved surveillance capability of the shipping traffic in Norwegian national waters. File downloaded from the Norwegian Defence Research Establishment (https://publications.ffi.no/nb/item/asset/dspace:7059/FFI-Facts_NorSat_Engelsk_web_v2.pdf). https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://schema.org/encodingFormat application/pdf https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://schema.org/license https://choosealicense.com/no-permission/ https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://schema.org/name NorSat-3: Ship Surveillance with a Navigation Radar Detector https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://schema.org/sdDatePublished 2022-10-17 19:53:16.725831+00:00 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Document https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/contentSize 392569 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/contentUrl https://api.rohub.org/api/resources/ed59cb0a-e359-4f95-932d-88375b08daa7/download/ https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/dateCreated 2022-12-07 07:59:22.749684+00:00 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/dateModified 2022-12-07 08:00:09.185793+00:00 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/description Major transportation surveillance protocols have not been specified with cyber securityin mind and therefore provide no encryption nor identification. These issues expose air and seatransport to false data injection attacks (FDIAs), in which an attacker modifies, blocks or emits fakesurveillance messages to dupe controllers and surveillance systems. There has been growing interestin conducting research on machine learning-based anomaly detection systems that address these newthreats. However, significant amounts of data are needed to achieve meaningful results with this typeof model. Raw, genuine data can be obtained from existing databases but need to be preprocessedbefore being fed to a model. Acquiring anomalous data is another challenge: such data is muchtoo scarce for both the Automatic Dependent Surveillance–Broadcast (ADS-B) and the AutomaticIdentification System (AIS). Crafting anomalous data by hand, which has been the sole methodapplied to date, is hardly suitable for broad detection model testing. This paper proposes an approachbuilt upon existing libraries and ideas that offers ML researchers the necessary tools to facilitatethe access and processing of genuine data as well as to automatically generate synthetic anomaloussurveillance data to constitute broad, elaborated test datasets. We demonstrate the usability of theapproach by discussing work in progress that includes the reproduction of related work, creation ofrelevant datasets and design of advanced anomaly https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/license https://choosealicense.com/no-permission/ https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/name Improved Testing of AI-Based Anomaly DetectionSystems Using Synthetic Surveillance Data https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://schema.org/sdDatePublished 2022-12-07 07:59:22.749684+00:00 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/resources/ed59cb0a-e359-4f95-932d-88375b08daa7 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#ConferencePaper https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ro-crate-metadata.json http://purl.org/dc/terms/conformsTo https://w3id.org/ro/crate/1.1 https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ro-crate-metadata.json http://schema.org/about https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a/ro-crate-metadata.json http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/CreativeWork https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://schema.org/contentUrl https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://schema.org/dateCreated 2023-01-10 20:22:38.390514+00:00 https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://schema.org/dateModified 2023-01-10 20:22:39.812665+00:00 https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://schema.org/description Research Object with sample AIS data (in-situ) https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://schema.org/license https://choosealicense.com/no-permission/ https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://schema.org/name Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://schema.org/sdDatePublished 2023-01-10 20:22:38.390514+00:00 https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/ro#ResearchObject https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/contentUrl https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/dateCreated 2022-12-09 15:13:48.102607+00:00 https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/dateModified 2022-12-13 15:54:20.069866+00:00 https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/description This Research Object contains AIS data (raw and pre-processed by Statsat AS, Norway). It is not public and has been provided by Statsat AS (Norway). If you are working at Simula, information on where to find pre-processed data on the EX3 is given in the Data RO (README.txt in the metadata folder). This dataset has been used for developing new machine learning algorithms for detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway) led by Simula Research Laboratory (Oslo, Norway). https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/keywords AIS https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/license https://choosealicense.com/no-permission/ https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/name AIS data prepared by Statsat AS for 2020 https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://schema.org/sdDatePublished 2022-12-09 15:13:48.102607+00:00 https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/ro#ResearchObject 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https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/dateCreated 2022-10-05 13:30:15.198183+00:00 https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/dateModified 2022-12-19 18:01:44.818563+00:00 https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/description The surveillance of maritime traffic is confronted with very important difficulties in detecting illegal activities at sea. In this article, we present the first results of a self-supervised learning method which aims to detect voluntary disconnec- tions of the identification’ system of vessels. By processing data from four Norwegian surveillance satellites, our lear- ning model aims to identify vessels suspected of illegal acti- vities such as fishing in protected areas or crossing econo- mic exclusion zones in real time. In this article, we present an approach based on self-supervised learning techniques, and experienced from real data. https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/encodingFormat application/pdf https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/keywords AIS https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/keywords Maritime Surveillance https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/keywords machine learning https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/keywords self-supervised https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/license https://choosealicense.com/no-permission/ https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/name Self-supervised learning for the detection of illegal actions during maritime traffic monitoring https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://schema.org/sdDatePublished 2022-10-05 13:30:15.198183+00:00 https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roterms#Paper 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