@article{ART003353348},
author={Hyejun Lee and Chin Jae Teuk},
title={Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning},
journal={The Korea Spatial Planning Review},
issn={1229-8638},
year={2026},
volume={129},
pages={3-21},
doi={10.15793/kspr.2026.129..001}
TY - JOUR
AU - Hyejun Lee
AU - Chin Jae Teuk
TI - Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning
JO - The Korea Spatial Planning Review
PY - 2026
VL - 129
IS - null
PB - 국토연구원
SP - 3
EP - 21
SN - 1229-8638
AB - Urban growth prediction has increasingly incorporated AI-driven modeling approaches in planning practice, yet questions remain regarding effective model conditions and their role in decision-making. This study examines the application of a ConvLSTM-based AI model to identify performance-sensitive conditions and to clarify the division of roles between AI and planning practice in urban growth management. Using time-series spatial data from 2000 to 2020 in Cheonan, Chungcheongnam-do, Korea, the model predicts urban growth for 2025 under varying spatial resolutions and input configurations. The results show that predictive performance improves at a 100×100 m resolution compared to 500×500 m, and when development constraints are explicitly included as input features. The findings suggest that AI captures latent spatial constraints to generate quantitatively grounded development potential, while planners retain a qualitative role in aligning outcomes with policy objectives and public acceptance. This study contributes to refining deep learning applications and proposes a complementary framework that integrates insights from the model into growth management.
KW - Deep Learning;Urban Growth Model;ConvLSTM;Land Use Change;Urban AI
DO - 10.15793/kspr.2026.129..001
ER -
Hyejun Lee and Chin Jae Teuk. (2026). Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning. The Korea Spatial Planning Review, 129, 3-21.
Hyejun Lee and Chin Jae Teuk. 2026, "Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning", The Korea Spatial Planning Review, vol.129, pp.3-21. Available from: doi:10.15793/kspr.2026.129..001
Hyejun Lee, Chin Jae Teuk "Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning" The Korea Spatial Planning Review 129 pp.3-21 (2026) : 3.
Hyejun Lee, Chin Jae Teuk. Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning. 2026; 129 3-21. Available from: doi:10.15793/kspr.2026.129..001
Hyejun Lee and Chin Jae Teuk. "Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning" The Korea Spatial Planning Review 129(2026) : 3-21.doi: 10.15793/kspr.2026.129..001
Hyejun Lee; Chin Jae Teuk. Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning. The Korea Spatial Planning Review, 129, 3-21. doi: 10.15793/kspr.2026.129..001
Hyejun Lee; Chin Jae Teuk. Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning. The Korea Spatial Planning Review. 2026; 129 3-21. doi: 10.15793/kspr.2026.129..001
Hyejun Lee, Chin Jae Teuk. Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning. 2026; 129 3-21. Available from: doi:10.15793/kspr.2026.129..001
Hyejun Lee and Chin Jae Teuk. "Urban Growth Prediction Using Deep Learning(ConvLSTM): Application to Growth Management Planning" The Korea Spatial Planning Review 129(2026) : 3-21.doi: 10.15793/kspr.2026.129..001
