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Harnessing Historical AIS data for Machine Learning ๐Ÿšข ๐Ÿค–๐Ÿ“–

Posted on June 15, 2023


In this recent webinar by Spire.com the audience is taken on a deep dive into the world of machine learning and its applications in the maritime industry. The presenters explore machine learning as a branch of artificial intelligence that utilizes data sets and algorithms to continually improve accuracy over time. The typical components of a machine learning model, including the decision process, error function, and method optimization process, are examined to demonstrate how they work together to analyze data sets and identify patterns.


Highlights:


  • Machine learning as a branch of AI for improving accuracy over time

  • Components of a machine learning model: decision process, error function, and method optimization process

  • Importance of historical data, focusing on AIS (automatic identification system) data in the maritime industry

  • Use cases of machine learning with AIS data: predictive maintenance, route planning optimization, emissions tracking, commodity trading analysis, identifying inefficiencies in supply chains, risk assessment, fraud detection, and risk mitigation in marine insurance

  • Requirements for effective use of AIS data in machine learning models

  • Freightflows showcases practical utilization of AIS data for building geospatial data sets and tracking commercial activities

The webinar emphasizes the importance of historical data for training machine learning models, particularly focusing on the utilization of AIS data. AIS, known as automatic identification system, enables ships to transmit their position and other relevant information to other vessels. Spire Maritime, a prominent player in the field, collects AIS data from its own satellite constellation and third-party providers to ensure comprehensive coverage. The various use cases for machine learning with AIS data are explored, such as predictive maintenance of vessels, route planning optimization, emissions tracking, predictive analysis for commodity trading, identification of bottlenecks and inefficiencies in supply chains, as well as risk assessment, fraud detection, and risk mitigation in the marine insurance industry.



The webinar also delves into the requirements for AIS data to be effectively used in machine learning models and highlights the role of Spire in this process. Spire boasts over 10 years of historical AIS data and continuous improvement in coverage. They offer the data in different formats, including raw NMEA format, decoded format, and combined post-processed format. Additionally, Spire provides flexible data delivery options and dedicated support teams to assist customers with their specific needs.


To provide a practical demonstration of AIS data utilization, Freightflows, a predictive analytics company specializing in global trade, shares insights. They explain how they leverage AIS data to build a comprehensive geospatial data set and create dynamic polygons representing port and berth areas. By analyzing ship movements and clustering the data, Freightflows accurately tracks commercial activities and provides valuable insights to its customers.


Overall, the webinar replay offers a comprehensive and informative exploration of machine learning, AIS data, and their applications in the maritime industry. It caters to a wide range of interests, including predictive maintenance, route optimization, emissions tracking, commodity trading, supply chain optimization, and marine insurance. Attendees have the opportunity to gain valuable insights and stay ahead in the rapidly evolving world of maritime technology.

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