# An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation

arXiv: https://arxiv.org/abs/2101.03169 · HTML: https://mlsanity.com/paper/2101.03169/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 10 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [shapeDTW: shape Dynamic Time Warping](https://mlsanity.com/paper/1606.01601/) (2016)
- [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018)
- [Inferring transportation modes from GPS trajectories using a convolutional neural network](https://mlsanity.com/paper/1804.02386/) (2018)
- [Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models](https://mlsanity.com/paper/1907.00058/) (2019)
- [Improving Deep Neural Network with Multiple Parametric Exponential Linear Units](https://mlsanity.com/paper/1606.00305/) (2016)
- [GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries](https://mlsanity.com/paper/2004.13653/) (2020)
- [Computing the Fr'echet Distance with a Retractable Leash](https://mlsanity.com/paper/1306.5527/) (2013)
- [Fast Frechet Distance Between Curves With Long Edges](https://mlsanity.com/paper/1710.10521/) (2017)

## Built on by (most cited first, 5 of 5)

- [UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings](https://mlsanity.com/paper/2407.12550/) (2024)
- [A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation](https://mlsanity.com/paper/2407.11084/) (2024)
- [Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation](https://mlsanity.com/paper/2403.08838/) (2024)
- [Auto-encoding GPS data to reveal individual and collective behaviour](https://mlsanity.com/paper/2312.00456/) (2023)
- [MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories](https://mlsanity.com/paper/2606.17978/) (2026)

## Abstract (arXiv)

To achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel trajectories. The computation of vessel trajectory similarity has recently attracted increasing attention in the maritime data mining research community. However, traditional shape- and warping-based methods often suffer from several drawbacks such as high computational cost and sensitivity to unwanted artifacts and non-uniform sampling rates, etc. To eliminate these drawbacks, we propose an unsupervised learning method which automatically extracts low-dimensional features through a convolutional auto-encoder (CAE). In particular, we first generate the informative trajectory images by remapping the raw vessel trajectories into two-dimensional matrices while maintaining the spatio-temporal properties. Based on the massive vessel trajectories collected, the CAE can learn the low-dimensional representations of informative trajectory images in an unsupervised manner. The trajectory similarity is finally equivalent to efficiently computing the similarities between the learned low-dimensional features, which strongly correlate with the raw vessel trajectories. Comprehensive experiments on realistic data sets have demonstrated that the proposed method largely outperforms traditional trajectory similarity computation methods in terms of efficiency and effectiveness. The high-quality trajectory clustering performance could also be guaranteed according to the CAE-based trajectory similarity computation results.