Deep Learning for Spatiotemporal Modeling of Urbanization
- URL: http://arxiv.org/abs/2112.09668v1
- Date: Fri, 17 Dec 2021 18:27:52 GMT
- Title: Deep Learning for Spatiotemporal Modeling of Urbanization
- Authors: Tang Li, Jing Gao, Xi Peng
- Abstract summary: Urbanization has a strong impact on the health and wellbeing of populations across the world.
Many spatial models have been developed using machine learning and numerical modeling techniques.
Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization.
- Score: 21.677957140614556
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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