βIntroduction
A little bit about where we stand.
Overview
Welcome to AISdb - your comprehensive gateway for Automatic Identification System (AIS) data uses and applications. AISdb is part of the AISViz - Making Vessels Tracking Data Available to Everyone project within the Marine Environmental Research Infrastructure for Data Integration and Application Network (MERIDIAN) initiative at Dalhousie University, designed to streamline the collection, processing, and analysis of AIS data, both in live-streaming scenarios and through historical records.
At the heart of AISdb is a robust database system built on SQLite, giving users a friendly Python interface to interact with. This interface simplifies tasks like database creation, data querying, processing, visualization, and even exporting data to CSV format for diverse uses. To cater to advanced needs, AISdb also supports using Postgres, offering superior concurrency handling and data-sharing capabilities for collaborative team environments.
One of the unique features of AISdb is its ability to enrich AIS datasets with environmental context. Users can seamlessly integrate oceanographic and bathymetric data in raster formats to bring depth to their analyses - quite literally, as the tool allows for incorporating seafloor depth data underneath vessel positions. Such versatility ensures that AISdb users can merge various environmental data points with AIS information, resulting in richer, multi-faceted maritime studies.
Academics, industry experts, and researchers will appreciate the significant repository that AISdb offers, including extensive Canadian AIS data of up to 100km from any side of the Canadian coast, stretching from January 2012 to the present, with monthly updates. This repository is regularly updated, offering raw and parsed data formats readily available for AIS-related research endeavors. By removing the preprocessing barrier, AISdb accelerates and simplifies the research process for everyone involved. Although AISdb is open-source and can be used with any AIS dataset, having asses to our pre-processed dataset requires a formal partnership with our research initiative.
AISdb is not just a storage and processing utility; it's a platform that facilitates robust research methods. Its Python interface with a RUST background paves the way for incorporating machine learning and deep learning techniques into vessel behavior modeling in an optimized way. This aspect of AISdb enhances the reproducibility and scalability of research, be it for academic exploration or practical industry applications.
The AISViz team, under the umbrella of the MERIDIAN project, is based in the Maritime Risk and Safety (MARS) research group at Dalhousie University. Funded by the Department of Fisheries and Oceans Canada (DFO), our mission revolves around democratizing AIS data use, making it accessible and understandable across multiple sectors, from government and academia to NGOs and the broader public. Besides, AISViz aims to introduce advanced machine learning applications into AIS data handling of AISdb. This innovation aims to streamline user interactions with AIS data, enhancing the user experience by simplifying data access and manipulation.
Our commitment goes beyond just providing tools. Through AISViz, we're opening doors to innovative research and policy development, targeting environmental conservation, maritime traffic management, and much more. Whether you're a professional in the field, an educator, or a maritime enthusiast, AISViz and its components, including AISdb, offer the knowledge and technology to deepen your understanding and significantly impact marine vessel tracking and the well-being of our oceans.
Our Team
Active Members
Gabriel Spadon has a Ph.D. in Computer Science and Computational Mathetics and is a postdoctoral fellow at Big Data Analytics at Dalhousie University. His current work focuses on neural-inspired models, graph-based deep learning, and complex network dynamics applied to multidisciplinary topics, such as human behavior and ocean sciences. His research includes Machine Learning, Deep Learning, Complex Networks, Time Series, and Data Mining.
Contact: gabriel@spadon.com.br or spadon@dal.ca
Jay Kumar has a Ph.D. in Computer Science and Technology and is a postdoctoral fellow at the Department of Industrial Engineering at Dalhousie University. For more than five years, he has been researching AI models for time-series data. His current research focuses on Recurrent Neural models, probabilistic modeling, and feature engineering data analytics applied to ocean vessel traffic. His research interests include Spatio-temporal Data Mining, Stochastic Modeling, Machine Learning, and Deep Learning.
Contact: jay.kumar@dal.ca
Jinkun Chen is a Ph.D. student in Computer Science at Dalhousie University, specializing in Explainable AI, Natural Language Processing (NLP), and Visualization. He earned a bachelor's degree in Computer Science with First-Class Honours from Dalhousie University. Jinkun is actively involved in research, working on advancing fairness, responsibility, trustworthiness, and explainability within Large Language Models (LLMs) and AI. In addition to his academic pursuits, Jinkun also serves as an AIS Data Analyst at MERIDIAN and is a valuable member of the HyperMatrix Lab and MALNIS Lab, all of which contribute to his research-related activities.
Contact: jinkun.chen@dal.ca or i@jinkunchen.com
Ronald Pelot has a Ph.D. in Management Sciences and is a Professor of Industrial Engineering at Dalhousie University. For the last 30 years, he and his team (MARS) have been working on developing new software tools and analysis methods for maritime traffic safety, coastal zone security, and marine spills. Their research methods include spatial risk analysis, vessel traffic modeling, data processing, pattern analysis, location models for response resource allocation, safety analyses, and cumulative shipping impact studies.
Contact: ronald.pelot@dal.ca
Former Members
Matthew Smith has a BSc degree in Applied Computer Science and specializes in managing and analyzing vessel tracking data, particularly AIS data. Until 2023, he served as the AIS data manager on the MERIDIAN project, where he supported research groups across Canada in accessing and utilizing AIS data. The data was used to answer a range of scientific queries, including the impact of shipping on underwater noise pollution and the danger posed to endangered marine mammals by vessel collisions.
Contact
We are passionate about fostering a collaborative and engaged community. We welcome your questions, insights, and feedback as vital components of our continuous improvement and innovation. Should you have any inquiries about AISdb, desire further information on our research, or wish to explore potential collaborations, please don't hesitate to contact us. Staying connected with users and researchers plays a crucial role in shaping the tool's development and ensuring it meets the diverse needs of our growing user base. You can easily contact our team via email or our GitHub team platform. In addition to addressing individual queries, we are committed to organizing webinars and workshops and presenting at conferences to share knowledge, gather feedback, and widen our outreach (stay tuned for more information about these). Together, let's advance the understanding and utilization of marine data for a brighter, more informed future in ocean research and preservation.
Citation
If our work has aided your research or inspired your project, we kindly ask you to acknowledge our contributions by citing our publication. For reference, please use the following citation:
Spadon, G., Kumar, J., Smith, M., Vela, S., Gehrmann, R., Eden, D., van Berkel, J., Soares, A., Fablet, R., Pelot, R., & Matwin, S. (2023). Building a Safer Maritime Environment Through Multi-Path Long-Term Vessel Trajectory Forecasting. arXiv preprint arXiv:2310.18948. https://doi.org/10.48550/arXiv.2310.18948
This citation ensures proper credit for the efforts and insights presented in our work and supports the integrity and continuity of academic discourse. We appreciate your support and recognition as we endeavor to enhance maritime safety through innovative research!
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