Generative adversarial networks for spatio-temporal data: A survey

Publication Year: 2020 Publication Type : JournalArticle


Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatiotemporal- based applications such as trajectory prediction, events generation and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this paper, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.


@article{gao2020generative, title={Generative adversarial networks for spatio-temporal data: A survey},
    author={Gao, Nan and Xue, Hao and Shao, Wei and Zhao, Sichen and Qin, Kyle Kai and Prabowo, Arian and Rahaman, Mohammad Saiedur and Salim, Flora D},
    journal={arXiv preprint arXiv:2008.08903},


Related Publications

RUP: Large Room Utilisation Prediction with carbon dioxide sensor
Type : JournalArticle
Show More
A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO 2 Sensor Data
Type : JournalArticle
Show More
Topical Event Detection on Twitter
Type : ConferenceProceeding
Show More

© 2021 Flora Salim - CRUISE Research Group.