Deep Learning for Spatiotemporal Modeling of Urbanization

TL;DR

This paper explores the application of deep learning techniques to model and predict urbanization patterns by treating geospatial data as images, demonstrating superior performance over traditional models.

Contribution

It introduces a novel deep spatial learning approach for urbanization prediction, leveraging image-based data representation and augmentation.

Findings

Deep learning outperforms classic models in urbanization prediction.

The model can generate multi-variable urbanization forecasts.

Enrichment of geospatial data improves model accuracy.

Abstract

Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effective public health planning. Many spatial urbanization models have been developed using classic machine learning and numerical modeling techniques. However, deep learning with its proven capacity to capture complex spatiotemporal phenomena has not been applied to urbanization modeling. Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization. We treat numerical geospatial data as images with pixels and channels, and enrich the dataset by augmentation, in order to leverage the high capacity of deep learning. Our resulting model can generate end-to-end multi-variable urbanization predictions, and outperforms a state-of-the-art classic machine learning urbanization model in preliminary comparisons.