Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.22734 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909557124497408 |
|---|---|
| author | Corvino, Michela Daffinà, Filippo Francalanci, Chiara Giacomazzi, Paolo Magliani, Martina Ravanelli, Paolo Stahl, Torbjorn |
| author_facet | Corvino, Michela Daffinà, Filippo Francalanci, Chiara Giacomazzi, Paolo Magliani, Martina Ravanelli, Paolo Stahl, Torbjorn |
| contents | Maritime AIS (Automatic Identification Systems) data serve as a valuable resource for studying vessel behavior. This study proposes a methodology to analyze route between maritime points of interest and extract geo-referenced standard routes, as maritime patterns of life, from raw AIS data. The underlying assumption is that ships adhere to consistent patterns when travelling in certain maritime areas due to geographical, environmental, or economic factors. Deviations from these patterns may be attributed to weather conditions, seasonality, or illicit activities. This enables maritime surveillance authorities to analyze the navigational behavior between ports, providing insights on vessel route patterns, possibly categorized by vessel characteristics (type, flag, or size). Our methodological process begins by segmenting AIS data into distinct routes using a finite state machine (FSM), which describes routes as seg-ments connecting pairs of points of interest. The extracted segments are ag-gregated based on their departure and destination ports and then modelled using iterative density-based clustering to connect these ports. The cluster-ing parameters are assigned manually to sample and then extended to the en-tire dataset using linear regression. Overall, the approach proposed in this paper is unsupervised and does not require any ground truth to be trained. The approach has been tested on data on the on a six-year AIS dataset cover-ing the Arctic region and the Europe, Middle East, North Africa areas. The total size of our dataset is 1.15 Tbytes. The approach has proved effective in extracting standard routes, with less than 5% outliers, mostly due to routes with either their departure or their destination port not included in the test areas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22734 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Methodology to extract Geo-Referenced Standard Routes from AIS Data Corvino, Michela Daffinà, Filippo Francalanci, Chiara Giacomazzi, Paolo Magliani, Martina Ravanelli, Paolo Stahl, Torbjorn Machine Learning Maritime AIS (Automatic Identification Systems) data serve as a valuable resource for studying vessel behavior. This study proposes a methodology to analyze route between maritime points of interest and extract geo-referenced standard routes, as maritime patterns of life, from raw AIS data. The underlying assumption is that ships adhere to consistent patterns when travelling in certain maritime areas due to geographical, environmental, or economic factors. Deviations from these patterns may be attributed to weather conditions, seasonality, or illicit activities. This enables maritime surveillance authorities to analyze the navigational behavior between ports, providing insights on vessel route patterns, possibly categorized by vessel characteristics (type, flag, or size). Our methodological process begins by segmenting AIS data into distinct routes using a finite state machine (FSM), which describes routes as seg-ments connecting pairs of points of interest. The extracted segments are ag-gregated based on their departure and destination ports and then modelled using iterative density-based clustering to connect these ports. The cluster-ing parameters are assigned manually to sample and then extended to the en-tire dataset using linear regression. Overall, the approach proposed in this paper is unsupervised and does not require any ground truth to be trained. The approach has been tested on data on the on a six-year AIS dataset cover-ing the Arctic region and the Europe, Middle East, North Africa areas. The total size of our dataset is 1.15 Tbytes. The approach has proved effective in extracting standard routes, with less than 5% outliers, mostly due to routes with either their departure or their destination port not included in the test areas. |
| title | A Methodology to extract Geo-Referenced Standard Routes from AIS Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.22734 |