  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 spatio-temporal-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.
