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April 29, 2026Environmental Modelling & SoftwareOpen Access

“Deep learning in modeling urban growth dynamics: a Systematic Literature Review”

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Authors

FHFarasath HasanJLJian LiuXLXintao LIU

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Overview

Systematic review examines deep learning applications for urban growth modeling, guiding future research directions.

Key Points

  • This literature review aims to systematically examine deep learning applications in urban growth modeling using the PRISMA approach.
  • Analyzed 2674 published papers from 2010 to 2025.
  • Categorized deep learning architectures into CNNs, RNNs, DFNNs, GANs, and Transformers, with classifications based on CA integration.
  • Identified challenges and future research directions in the field.
  • Hybrid models confined to CNNs, RNNs, and DFNNs.
  • Identified limitations in data integration and prolonged computation time for hyperparameter optimization.
  • Seven future research directions focusing on feature learning and public perception modeling.

Cite This Study

Hasan et al. (2026) studied this question.

synapsesocial.com/papers/69f19f16edf4b46824806295https://doi.org/10.1016/j.envsoft.2026.107000
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