Authors
Vincenzo Capone, Angelo Casolaro, Francesco Camastra
Journal
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.
Description
The article “Deep Learning for Regular Raster Spatio-Temporal Prediction: An Overview” presents a comprehensive review of deep learning techniques designed to model and forecast spatio-temporal data organized in regular raster formats.
These data types, common in fields such as environmental monitoring, remote sensing, traffic analysis, and video processing, capture dynamic processes evolving across space and time. The study systematically analyzes major deep learning architectures applied to this domain, discussing their strengths, limitations, and comparative performance against traditional statistical approaches.
By highlighting current challenges and outlining promising research directions, the work serves as a valuable reference for scientists and practitioners developing advanced predictive models for spatially structured time series