# GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection

arXiv: https://arxiv.org/abs/1912.00682 · HTML: https://mlsanity.com/paper/1912.00682/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 16 of 16)

- [TrAISformer -- A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction](https://mlsanity.com/paper/2109.03958/) (2021)
- [TrackGPT -- A generative pre-trained transformer for cross-domain entity trajectory forecasting](https://mlsanity.com/paper/2402.00066/) (2024)
- [A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels](https://mlsanity.com/paper/2207.05514/) (2022)
- [MobilityDL: A Review of Deep Learning From Trajectory Data](https://mlsanity.com/paper/2402.00732/) (2024)
- [Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities](https://mlsanity.com/paper/2307.10803/) (2023)
- [Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning](https://mlsanity.com/paper/2310.15586/) (2023)
- [Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models](https://mlsanity.com/paper/2401.13098/) (2024)
- [AIS-LLM: A Unified Framework for Maritime Trajectory Prediction, Anomaly Detection, and Collision Risk Assessment with Explainable Forecasting](https://mlsanity.com/paper/2508.07668/) (2025)
- [Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Trajectories](https://mlsanity.com/paper/2107.01557/) (2021)
- [Deep learning for Lagrangian drift simulation at the sea surface](https://mlsanity.com/paper/2211.09818/) (2022)
- [DiffuTraj: A Stochastic Vessel Trajectory Prediction Approach via Guided Diffusion Process](https://mlsanity.com/paper/2410.09550/) (2024)
- [Role and Integration of Image Processing Systems in Maritime Target Tracking](https://mlsanity.com/paper/2206.12809/) (2022)
- [Geospatial Data Fusion: Combining Lidar, SAR, and Optical Imagery with AI for Enhanced Urban Mapping](https://mlsanity.com/paper/2412.18994/) (2024)
- [An Automated Tip-and-Cue Framework for Optimized Satellite Tasking and Visual Intelligence](https://mlsanity.com/paper/2512.09670/) (2025)
- [Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection](https://mlsanity.com/paper/2512.20086/) (2025)
- [Context-Aware Autoencoders for Anomaly Detection in Maritime Surveillance](https://mlsanity.com/paper/2602.00124/) (2026)

## Abstract (arXiv)

Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach -- referred to as GeoTrackNet -- for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks and a contrario detection to detect abnormal events. The neural network provides a new means to capture complex and heterogeneous patterns in vessels' behaviours, while the textita contrario detector takes into account the fact that the learnt distribution may be location-dependent. Experiments on a real AIS dataset comprising more than 4.2 million AIS messages demonstrate the relevance of the proposed method compared with state-of-the-art schemes.