Research Article
T2I-CycleGAN: A CycleGAN for Maritime Road Network Extraction from Crowdsourcing Spatio-Temporal AIS Trajectory Data
@INPROCEEDINGS{10.1007/978-3-030-67540-0_12, author={Xuankai Yang and Guiling Wang and Jiahao Yan and Jing Gao}, title={T2I-CycleGAN: A CycleGAN for Maritime Road Network Extraction from Crowdsourcing Spatio-Temporal AIS Trajectory Data}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Unsupervised learning Generative adversarial network AIS data Road network Spatio-temporal data mining Trajectory data mining}, doi={10.1007/978-3-030-67540-0_12} }- Xuankai Yang
Guiling Wang
Jiahao Yan
Jing Gao
Year: 2021
T2I-CycleGAN: A CycleGAN for Maritime Road Network Extraction from Crowdsourcing Spatio-Temporal AIS Trajectory Data
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_12
Abstract
Maritime road network is composed of detailed maritime routes and is vital in many applications such as threats detection, traffic control. However, the vessel trajectory data, or Automatic Identification System (AIS) data, are usually large in scale and collected with different sampling rates. And, what’s more, it is difficult to obtain enough accurate road networks as paired training datasets. It is a huge challenge to extract a complete maritime road network from such data that matches the actual route of the ship. In order to solve these problems, this paper proposes an unsupervised learning-based maritime road network extraction model T2I-CycleGAN based on CycleGAN. The method translates trajectory data into unpaired input samples for model training, and adds dense layer to the CycleGAN model to handle trajectories with different sampling rates. We evaluate the approach on real-world AIS datasets in various areas and compare the extracted results with the real ship coordinate data in terms of connectivity and details, achieving effectiveness beyond the most related work.
