AI-Driven Multi-Model Classification of Rural Settlements for Targeted Rural Revitalization: A Case Study of Gaoqing County, Shandong Province, China

Jing He, Xinlei Wang, Yingtao Qi (), Jinghan Jiang, Dian Zhou, Ding Ma and Jing Ying
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Jing He: School of Humanities and Social Science, Xi’an Jiaotong University, Xi’an 710049, China
Xinlei Wang: School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yingtao Qi: School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Jinghan Jiang: School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Dian Zhou: School of Human Settlement and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Ding Ma: Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Jing Ying: Ningbo Institute of Technology, School of Economics, Zhejiang University, Hangzhou 310027, China

Land, 2025, vol. 14, issue 12, 1-32

Abstract: Rural settlements are the fundamental socio-economic units of China’s countryside. In line with national strategies that emphasize place-based and category-specific pathways for rural revitalization, accurate classification of rural settlements is essential for differentiated planning and policy delivery. However, given the sheer number of settlements, manual classification is time-consuming and resource-intensive, limiting scalability. This study proposes an AI-driven, multi-model framework to automate rural settlement classification with high stability and accuracy. First, informed by a rigorous literature review, we construct a multidimensional indicator system that integrates natural conditions, socio-economic attributes, and land-use factors to capture spatial and functional characteristics at the settlement scale. Using Gaoqing County (Shandong Province) as the study area, we collect and curate survey data and apply outlier detection for preprocessing. We then benchmark multiple machine learning models and find that algorithms with native handling of missing values perform markedly better—a critical advantage given the prevalence of missingness in survey-based datasets. Finally, we assemble the three best-performing models—LightGBM, CatBoost, and XGBoost—into a weighted-voting ensemble, achieving an overall classification accuracy of approximately 88%. The results demonstrate that the refined indicator system, coupled with a multi-model ensemble, substantially improves both accuracy and robustness. This work provides a methodological foundation and empirical evidence to support differentiated planning and targeted rural revitalization at the settlement level, offering a scalable blueprint for broader regional and national implementation.

Keywords: rural settlements; automated classification; machine learning; indicator system; targeted rural revitalization (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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