Anomaly Detection white paper
Improving Maritime Situational Awareness Through Big Data Analytics, Machine Learning and Artificial Intelligence
MT Research, MarineTraffic.com, 2019
Abstract
This white paper is intended for individuals who would like to learn more about Maritime Situational Awareness (i.e., the ability to combine different data sources and perform data analysis in order to derive new information such as classifying activities at sea). Within this scope we discuss the needs, expectations, challenges, explore the state of the art and present the MarineTraffic MSA service.
The main topics covered within include
- Why we designed the MarineTraffic MSA and anomaly detection service
- What were the user needs and how did these translate into technical requirements
- What were the difficulties faced and where did the state of the art fall short
- Reasoning behind our decisions and breakthroughs achieved
- An overview of the current service and description of its capabilities
- Next steps and future work
To download the white-paper please register first here and a download link will be sent to your email.
This project has received funding from the European Union’s Ho-rizon 2020 research and innovation programme under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732310.
.webp)
.webp)
Previous
Next
Navigating through dense waters: a toolbox for creating maritime density maps
A Big Data framework for Modelling and Simulating high-resolution hydrodynamic models in sea harbours
Modelling and simulating vessel emissions in real time based on terrestrial AIS data
Real-time maritime anomaly detection: detecting intentional AIS switch-off
A comparison of supervised learning schemes for the detection of search and rescue (SAR) vessel patterns
BigDataOcean Project: Early Anomaly Detection from Big Maritime Vessel Traffic Data
Countering Real-Time Stream Poisoning: An Architecture for Detecting Vessel Spoofing in Streams of AIS Data
Determining the bilge water waste risk and management in the Gulf of Antalya by the Monte Carlo method
FleetMon Supports the Development of Environmental Impact Assessment on the Brazilian Coast
Scrapping Probabilities and Committed CO2 Emissions of the International Ship Fleet
Scalable In-Database Machine Learning for the Prediction of Port-to-Port Routes
How a real-time-based sea traffic forecast helps to organize and optimize the flow of maritime goods
Generating a node in an AIS-based routing graph for improved Estimated Time of Arrival. (Big) Data challenge: using AIS for generating a routing graph
Composition, spatial distribution and sources of macro-marine litter on the Gulf of Alicante seafloor (Spanish Mediterranean)
Climate change, non-indigenous species and shipping: assessing the risk of species introduction to a high-Arctic archipelago
The Big Picture: An Improved Method for Mapping Shipping Activities
GMSA: A Digital Twin Application for Maritime Route and Event Forecasting
Patterns of Life : Global Inventory for maritime mobility patterns
Require AIS data for your project?
Please reach out to us to discuss which Kpler offering will support your research project or academic study in the best possible and efficient way.
