# Deep Learning for Spatiotemporal Modeling of Urbanization

arXiv: https://arxiv.org/abs/2112.09668 · HTML: https://mlsanity.com/paper/2112.09668/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation](https://mlsanity.com/paper/1801.05746/) (2018)

## Built on by (most cited first, 5 of 5)

- [Are Data-driven Explanations Robust against Out-of-distribution Data?](https://mlsanity.com/paper/2303.16390/) (2023)
- [DEAL: Disentangle and Localize Concept-level Explanations for VLMs](https://mlsanity.com/paper/2407.14412/) (2024)
- [Topology-aware Robust Optimization for Out-of-distribution Generalization](https://mlsanity.com/paper/2307.13943/) (2023)
- [Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales](https://mlsanity.com/paper/2411.00132/) (2024)
- [PathletRL++: Optimizing Trajectory Pathlet Extraction and Dictionary Formation via Reinforcement Learning](https://mlsanity.com/paper/2412.03715/) (2024)

## Abstract (arXiv)

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.