T-SAR project
type: Nanopublication
type: RoCrateNanopub
has assertion: assertion
has provenance: provenance
has publication info: pubinfo
created: 2026-03-03T15:23:10.308+01:00
creator: RoCrateBot

Applied sciences
type: DefinedTerm

Earth observation
type: DefinedTerm

VTexplorer API documentation.
type: Resource
type: MediaObject
type: SoftwareDocumentation
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-12-08 11:55:50.363662+00:00
date modified: 2022-12-19 17:59:56.908974+00:00
description: VTexplorer API (https://www.vtexplorer.com) documentation. This documentation can be useful to understand how AIS data can be processed.
encoding format: text/html
keywords: API
license: no-permission
sd date published: 2022-12-08 11:55:50.363662+00:00

NORAD Two-Line Element Set Format
type: Document
type: Resource
type: MediaObject
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-12-24 09:43:33.106536+00:00
date modified: 2022-12-24 09:43:33.827515+00:00
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).
keywords: TLE
license: no-permission
sd date published: 2022-12-24 09:43:33.106536+00:00

TSAR Overview
type: Document
type: Resource
type: MediaObject
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-12-03 12:22:11.080763+00:00
date modified: 2022-12-03 12:22:11.948165+00:00
description: Main document (Google doc) provided when willing to start with TSAR Overview.
license: no-permission
sd date published: 2022-12-03 12:22:11.080763+00:00

Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches
type: Paper
type: Resource
type: MediaObject
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-10-19 12:26:00.283770+00:00
date modified: 2022-12-19 18:00:27.575120+00:00
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.
keywords: AIS
keywords: anomaly detection
keywords: automatic identification system
keywords: maritime safety
keywords: maritime security
license: no-permission
sd date published: 2022-10-19 12:26:00.283770+00:00

TSAR google drive project area
type: Resource
type: MediaObject
type: Thing
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-10-05 13:34:23.536054+00:00
date modified: 2022-12-13 15:27:48.034278+00:00
description: Internally shared google drive with data and documents for T-SAR project
keywords: folder
license: no-permission
sd date published: 2022-10-05 13:34:23.536054+00:00

TSAR project overview
type: Resource
type: MediaObject
type: PresentationDigitalDocument
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-12-19 17:55:01.781065+00:00
date modified: 2022-12-19 18:00:49.447899+00:00
description: Slides (private) presenting the T-SAR project.
keywords: AIS
keywords: Vessel
license: no-permission
sd date published: 2022-12-19 17:55:01.781065+00:00

Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning
type: Paper
type: Resource
type: MediaObject
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-12-19 19:08:04.054398+00:00
date modified: 2022-12-19 19:08:05.371245+00:00
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.
license: no-permission
sd date published: 2022-12-19 19:08:04.054398+00:00

Two-Line Element Set (wikipedia)
type: Document
type: Resource
type: MediaObject
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-12-26 10:22:06.052178+00:00
date modified: 2022-12-26 10:22:07.502404+00:00
description: Description of Two-Line Element Set (TLE) from Wikipedia.
keywords: wikipedia
license: no-permission
sd date published: 2022-12-26 10:22:06.052178+00:00

repo set up for reproducibility purposes (private gitlab)
type: Resource
type: MediaObject
type: SWDocumentation
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2023-01-05 14:54:54.005940+00:00
date modified: 2023-01-05 14:54:57.935510+00:00
description: Gitlab repository set up for reproducibility purposes.
keywords: gitlab
license: no-permission
sd date published: 2023-01-05 14:54:54.005940+00:00

Marivisu v2
type: Resource
type: MediaObject
type: SWDocumentation
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-10-05 13:37:31.278559+00:00
date modified: 2022-10-05 13:37:32.047690+00:00
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).
license: no-permission
sd date published: 2022-10-05 13:37:31.278559+00:00

Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning
type: Resource
type: MediaObject
type: SWDocumentation
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-10-05 13:38:39.041164+00:00
date modified: 2022-10-05 13:38:39.630828+00:00
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.
license: no-permission
sd date published: 2022-10-05 13:38:39.041164+00:00

vesseltype_identification_dae (gitlab private repo)
type: Resource
type: MediaObject
type: SWDocumentation
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-11-15 13:19:46.330017+00:00
date modified: 2022-11-15 13:19:53.225356+00:00
description: Private repository containing Anomaly Detection in Vessels Trajectories using Context-Aware Autoencoders
license: no-permission
sd date published: 2022-11-15 13:19:46.330017+00:00

TSAR meeting notes (hackmd)
type: Resource
type: HowTo
type: MediaObject
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-10-06 08:13:34.706608+00:00
date modified: 2023-01-18 15:58:48.133720+00:00
description: Here we gather information about the project (notes taken during meetings, etc.). We use hackmd.io and text is written in markdown.
keywords: folder
license: no-permission
sd date published: 2022-10-06 08:13:34.706608+00:00

Jørgen Schartum Dokken
type: Agent
affiliation: Simula Research Laboratory
orcid: 0000-0001-6489-8858

Anne Fouilloux
type: Agent
affiliation: Simula Research Laboratory
orcid: 0000-0002-1784-2920

Thomas Roehr
type: Agent
affiliation: Simula Research Laboratory

Simula Research Laboratory
type: Organization
type: Agent

https://w3id.org/ro-id/42613c9d-9bea-4bc9-b475-1ac886a05d57
type: ResearchObject
type: ForkedRO
type: LiveRO
forked at time: 2023-03-16 07:34:14.812840+00:00

T-SAR project
type: ResearchObject
type: LiveRO
type: Dataset
type: ExecutableResearchObject
about: Applied sciences
about: Earth observation
author: Jørgen Schartum Dokken
author: Anne Fouilloux
author: Thomas Roehr
content size: 6363978
copyright holder: Simula Research Laboratory
date created: 2022-10-04 13:39:13.980365+00:00
date modified: 2025-10-18 11:20:47.365546+00:00
date published: 2022-10-04 13:39:13.980365+00:00
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.
encoding format: application/ld+json
keywords: AIS
keywords: Automatic Identification System
keywords: machine learning
keywords: maritime surveillance
keywords: self-supervised learning
license: no-permission
publisher: Simula Research Laboratory
creation mode: MANUAL
template: http://w3id.org/ro/earth-science#ExecutableResearchObjectTemplate
cite-as: Pierre Bernabé, Anne Fouilloux, Jørgen Schartum Dokken, Thomas Roehr, and Dusica Marijan. "T-SAR project." ROHub. Oct 04 ,2022. https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a.

output
type: Folder
type: Dataset

biblio
type: Folder
type: Dataset

TSAR_publications
type: Folder
type: Dataset
description: papers, conference proceeding generated by the TSAR project.
has part: TSAR project overview
has part: Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning
has part: Self-supervised learning for the detection of illegal actions during maritime traffic monitoring

documentation
type: Folder
type: Dataset
description: Documentation and existing information about surveillance and detection of anomalies using Automatic Identification System data (ground and satellite).
has part: VTexplorer API documentation.
has part: NORAD Two-Line Element Set Format
has part: Two-Line Element Set (wikipedia)

tool
type: Folder
type: Dataset
has part: repo set up for reproducibility purposes (private gitlab)
has part: Marivisu v2
has part: Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning
has part: vesseltype_identification_dae (gitlab private repo)

papers
type: Folder
type: Dataset
description: Bibliography collected on Automatic Identification System and detection of anomalies from AIS data (ground/satellite).
has part: Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches

input
type: Folder
type: Dataset
has part: TSAR Overview
has part: TSAR google drive project area
has part: TSAR meeting notes (hackmd)

labelled-messages.png
type: Sketch
type: Resource
type: MediaObject
author: Anne Fouilloux
content size: 1538071
creator: Anne Fouilloux
date created: 2022-10-06 08:54:49.163358+00:00
date modified: 2022-10-06 08:57:06.885956+00:00
description: Distribution of samples on the surface of the globe.
encoding format: image/png
license: no-permission
sd date published: 2022-10-06 08:54:49.163358+00:00

NorSat-3: Ship Surveillance with a Navigation Radar Detector
type: Document
type: Resource
type: MediaObject
author: Anne Fouilloux
content size: 4435867
creator: Anne Fouilloux
date created: 2022-10-17 19:53:16.725831+00:00
date modified: 2022-10-17 19:53:19.988447+00:00
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).
encoding format: application/pdf
license: no-permission
sd date published: 2022-10-17 19:53:16.725831+00:00

Improved Testing of AI-Based Anomaly DetectionSystems Using Synthetic Surveillance Data
type: Resource
type: MediaObject
type: ConferencePaper
author: Anne Fouilloux
content size: 392569
creator: Anne Fouilloux
date created: 2022-12-07 07:59:22.749684+00:00
date modified: 2022-12-07 08:00:09.185793+00:00
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
license: no-permission
sd date published: 2022-12-07 07:59:22.749684+00:00

Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance
type: ResearchObject
type: Resource
type: MediaObject
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2023-01-10 20:22:38.390514+00:00
date modified: 2023-01-10 20:22:39.812665+00:00
description: Research Object with sample AIS data (in-situ)
license: no-permission
sd date published: 2023-01-10 20:22:38.390514+00:00

AIS data prepared by Statsat AS for 2020
type: ResearchObject
type: Resource
type: MediaObject
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-12-09 15:13:48.102607+00:00
date modified: 2022-12-13 15:54:20.069866+00:00
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).
keywords: AIS
license: no-permission
sd date published: 2022-12-09 15:13:48.102607+00:00

computer science
type: Domain
norm score: 34.84162895927601
score: 7.699999999999999

physics
type: Domain
norm score: 9.049773755656108
score: 2.0

air traffic
type: Concept
norm score: 4.008152173913044
score: 5.9

Political parties and movements
type: IPTC
path: Politics/Political process/Political parties and movements

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type: Phrase
norm score: 4.06015037593985
score: 2.7

threat data generation
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norm score: 8.1203007518797
score: 5.4

North Cape
type: Place
wikidata: Q4069

AIS
type: Lemma
norm score: 9.079903147699758
score: 7.5

transport
type: Lemma
norm score: 6.65859564164649
score: 5.5

Navigation Radar Detector
type: Lemma
norm score: 6.537530266343826
score: 5.4

transport infrastructure
type: Phrase
norm score: 12.330827067669171
score: 8.2

technology
type: Concept
norm score: 2.921195652173913
score: 4.3

Greenland
type: Place
wikidata: Q223

artificial immune system
type: Concept
norm score: 8.152173913043478
score: 12.0

other earth sciences
type: FieldOfResearch
norm score: 54.45134430988258
score: 0.9434656500816345

geosciences
type: NASA
norm score: 51.00287938193204
score: 0.6229659914970398

antenna
type: Concept
norm score: 4.076086956521739
score: 6.0

geophysics
type: NASA
norm score: 51.00287938193204
score: 0.6229659914970398

communications and radar
type: NASA
norm score: 48.99712061806796
score: 0.5984669923782349

AIS receiver
type: Phrase
norm score: 9.924812030075188
score: 6.6

equipment
type: Concept
norm score: 3.192934782608696
score: 4.7

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.
type: Sentence
norm score: 23.821656050955415
score: 18.7

vessel
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norm score: 4.823369565217392
score: 7.1

self-driving car
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norm score: 9.62406015037594
score: 6.4

satellite
type: Concept
norm score: 6.929347826086956
score: 10.2

crime
type: Domain
norm score: 14.027149321266968
score: 3.1

Waterway and maritime transport
type: IPTC
path: Economy, business and finance/Economic sector/Transport/Waterway and maritime transport

Russia
type: Place
wikidata: Q159

earth sciences
type: FieldOfResearch
norm score: 54.45134430988258
score: 0.9434656500816345

bolt head
type: Concept
norm score: 4.755434782608696
score: 7.0

Nrd payload
type: Phrase
norm score: 3.9097744360902253
score: 2.6

artificial intelligence
type: Concept
norm score: 5.842391304347826
score: 8.6

car
type: Lemma
norm score: 6.779661016949153
score: 5.6

Air and space accident and incident
type: IPTC
path: Disaster, accident and emergency incident/Accident and emergency incident/Transport accident and incident/Air and space accident and incident

Education
type: IPTC
path: Education

Linguistics
type: IPTC
path: Science and technology/Social sciences/Linguistics

vessel
type: Lemma
norm score: 6.65859564164649
score: 5.5

Svalbard
type: Place
wikidata: Q842829

Juvenile delinquency
type: IPTC
path: Society/Social problem/Juvenile delinquency

receiver
type: Lemma
norm score: 5.326876513317193
score: 4.4

satellite
type: Lemma
norm score: 7.627118644067797
score: 6.3

during the summer of
type: TimeReference

https://w3id.org/ro-id/enrichment/77e11ce5-f594-4bcd-be70-16d67a6e14a1
type: Sentence
name: 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.
norm score: 16.178343949044585
score: 12.7

detection
type: Concept
norm score: 4.1440217391304355
score: 6.1

https://w3id.org/ro-id/enrichment/798f9c74-5063-4fea-a5d5-c91662c7f759
type: Sentence
name: In FFI proposed to deploy a microsatellite that could detect the navigational radar sig nals a Navigation Radar Detector (NRD). Now the Norwegian Space Centre has offered to include an experimental NRD on the NorSat microsatellite which is due for launch during spring .
norm score: 6.114649681528663
score: 4.8

Computer crime
type: IPTC
path: Crime, law and justice/Crime/Computer crime

linguistics
type: Domain
norm score: 24.43438914027149
score: 5.4

tsar project
type: Phrase
norm score: 14.887218045112782
score: 9.9

NorSat will be able to observe globally with both the AIS receiver and the radar detector, but the radar detector will mainly be used in the northern areas.
type: Sentence
norm score: 6.369426751592357
score: 5.0

Nrd antenna
type: Phrase
norm score: 4.661654135338346
score: 3.1

AIS message
type: Phrase
norm score: 4.511278195488722
score: 3.0

The yellow and orange symbols are AIS observations from the satellite which came in addition to the existing AIS observations (green and blue) from the Coastal Administration ground based sensors.
type: Sentence
norm score: 4.45859872611465
score: 3.5

https://w3id.org/ro-id/enrichment/8c8fd292-f36f-4717-9957-574c76b29ef7
type: Sentence
name: 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.
norm score: 43.05732484076432
score: 33.8

Satellite technology
type: IPTC
path: Economy, business and finance/Economic sector/Computing and information technology/Satellite technology

sensor
type: Concept
norm score: 6.521739130434783
score: 9.6

engineering
type: NASA
norm score: 48.99712061806796
score: 0.5984669923782349

India
type: Place
wikidata: Q668

AIS transmitter
type: Phrase
norm score: 3.458646616541353
score: 2.3

NorSat
type: Lemma
norm score: 7.869249394673124
score: 6.5

telecommunications
type: Domain
norm score: 17.64705882352941
score: 3.9

data
type: Lemma
norm score: 6.053268765133172
score: 5.0

oceanography
type: FieldOfResearch
norm score: 45.54865569011742
score: 0.789210855960846

data
type: Concept
norm score: 4.347826086956522
score: 6.4

French Guiana
type: Place
wikidata: Q3769

attack
type: Concept
norm score: 3.940217391304348
score: 5.8

car
type: Concept
norm score: 5.63858695652174
score: 8.3

tsar
type: Concept
norm score: 7.5407608695652195
score: 11.100000000000001

background satellite
type: Phrase
norm score: 3.7593984962406015
score: 2.5

Ministry of Defence
type: Organization
wikidata: Q1788820

radar detector
type: Phrase
norm score: 9.473684210526315
score: 6.3

False Data Injection Attack
type: Lemma
norm score: 11.138014527845035
score: 9.2

during spring
type: TimeReference

cyberattack
type: Concept
norm score: 2.921195652173913
score: 4.3

tsar
type: Lemma
norm score: 6.416464891041163
score: 5.3

Military equipment
type: IPTC
path: Politics/Government/Defence/Military equipment

detector
type: Lemma
norm score: 7.263922518159807
score: 6.0

generation
type: Concept
norm score: 5.16304347826087
score: 7.6

conveyance
type: Concept
norm score: 4.959239130434783
score: 7.3

radar
type: Lemma
norm score: 5.569007263922518
score: 4.6

air traffic management
type: Phrase
norm score: 11.278195488721805
score: 7.5

generation
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norm score: 7.02179176755448
score: 5.8

earth sciences
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norm score: 45.54865569011742
score: 0.789210855960846

radar
type: Concept
norm score: 7.812500000000001
score: 11.5

Transport
type: IPTC
path: Economy, business and finance/Economic sector/Transport

Self-supervised learning for the detection of illegal actions during maritime traffic monitoring
type: Paper
type: Resource
type: MediaObject
author: Anne Fouilloux
creator: Anne Fouilloux
date created: 2022-10-05 13:30:15.198183+00:00
date modified: 2022-12-19 18:01:44.818563+00:00
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.
encoding format: application/pdf
keywords: AIS
keywords: Maritime Surveillance
keywords: machine learning
keywords: self-supervised
license: no-permission
sd date published: 2022-10-05 13:30:15.198183+00:00

Anne Fouilloux
type: Agent

Dusica Marijan
type: Agent
affiliation: Simula Research Laboratory

Pierre Bernabé
type: Agent
affiliation: Simula Research Laboratory

service-account-enrichment
type: Agent

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has algorithm: RSA
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