https://github.com/leopits/vessels-anomaly-detection-with-ais-data

Anomaly detection from ships' Automatic Identification System (AIS) data
https://github.com/leopits/vessels-anomaly-detection-with-ais-data

ais-data anomaly-detection-algorithm python ships

Last synced: 3 months ago
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Anomaly detection from ships' Automatic Identification System (AIS) data

• Host: GitHub
• URL: https://github.com/leopits/vessels-anomaly-detection-with-ais-data
• Owner: LeoPits
• Created: 2023-10-24T13:01:04.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-02T13:06:41.000Z (6 months ago)
• Last Synced: 2024-11-02T14:18:09.603Z (6 months ago)
• Topics: ais-data, anomaly-detection-algorithm, python, ships
• Language: Jupyter Notebook
• Homepage:
• Size: 13.5 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Vessels anomaly detection with AIS data

## Introduction

We takes inspiration from  TREAD* (Traffic Route Extraction and Anomaly Detection)[1], that

learns a statistical model for maritime traffic from AIS data in an unsupervised way

## Dataset

National Oceanic and Atmospheric Administration: https://marinecadastre.gov/accessais

The information contained in the AIS data (generally) are of dynamic and static type

![dataset](https://github.com/LeoPits/AIS_anomaly_detection/assets/19689590/61b3d2a2-d571-4d36-bea3-f4ad43aa7a58)

Area of interest: **Hawaii**

A bounding box is selected and it corresponds to the (chosen) specific area under surveillance

![image](https://github.com/LeoPits/AIS_anomaly_detection/assets/19689590/a52f0ff1-dbca-4253-9bdf-86fc5f8004c2) 

## Waypoints identification

Waypoints (WPs): identify either stationary points (ports, offshore platforms, etc.), entry points and exit points.

WPs  identification is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method

Note: The hyperparameters of DBSCAN are tuned manually.

![image](https://github.com/LeoPits/AIS_anomaly_detection/assets/19689590/a9aaef02-f73f-4803-98a2-aba101fd8613)

## Waypoints identification

Once the waypoints are learned, a route can be built by clustering the extracted vessel flows, which connect:

* Two ports

* Entry point and port 

* Port and Exit point 

* Entry point and Exit point (i.e., transit routes)

![image](https://github.com/LeoPits/AIS_anomaly_detection/assets/19689590/4fb8a983-2791-4603-b7d3-30a3011d17f3)

## PoL learning (work in progress)

It is necessary to remove outliers from the routes to identify normal traffic.

In TREAD, it is used the stochastic method **Kernel Density Estimation (KDE)**.

![image](https://github.com/LeoPits/AIS_anomaly_detection/assets/19689590/c95ea577-764b-41f3-aef4-9402470dcaad)

## References

[1] *Pallotta, Giuliana, Vespe, Michele and Bryan, Karna. "Vessel pattern knowledge

discovery from AIS data: A framework for anomaly detection and route prediction."

Entropy 15.6 (2013): 2218-2245.