Deep Learning for Regular Raster Spatio-Temporal Prediction: An Overview

Abstract

The raster is the most common type of spatio-temporal data, and it can be either regularly or irregularly spaced. Spatio-temporal prediction on regular raster data is crucial for modelling and understanding dynamics in disparate realms, such as environment, traffic, astronomy, remote sensing, gaming and video processing, to name a few. Historically, statistical and classical machine learning methods have been used to model spatio-temporal data, and, in recent years, deep learning has shown outstanding results in regular raster spatio-temporal prediction. This work provides a self-contained review about effective deep learning methods for the prediction of regular raster spatio-temporal data. Each deep learning technique is described in detail, underlining its advantages and drawbacks. Finally, a discussion of relevant aspects and further developments in deep learning for regular raster spatio-temporal prediction is presented.