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Identification of rural courtyards’ utilization status using deep learning and machine learning methods on unmanned aerial vehicle images in north China

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  • Architecture and Human Behavior
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Abstract

The issue of unoccupied or abandoned homesteads (courtyards) in China emerges given the increasing aging population, rapid urbanization and massive rural-urban migration. From the aspect of rural vitalization, land-use planning, and policy making, determining the number of unoccupied courtyards is important. Field and questionnaire-based surveys were currently the main approaches, but these traditional methods were often expensive and laborious. A new workflow is explored using deep learning and machine learning algorithms on unmanned aerial vehicle (UAV) images. Initially, features of the built environment were extracted using deep learning to evaluate the courtyard management, including extracting complete or collapsed farmhouses by Alexnet, detecting solar water heaters by YOLOv5s, calculating green looking ratio (GLR) by FCN. Their precisions exceeded 98%. Then, seven machine learning algorithms (Adaboost, binomial logistic regression, neural network, random forest, support vector machine, decision trees, and XGBoost algorithms) were applied to identify the rural courtyards’ utilization status. The Adaboost algorithm showed the best performance with the comprehensive consideration of most metrics (Accuracy: 0.933, Precision: 0.932, Recall: 0.984, F1-score: 0.957). Results showed that identifying the courtyards’ utilization statuses based on the courtyard built environment is feasible. It is transferable and cost-effective for large-scale village surveys, and may contribute to the intensive and sustainable approach to rural land use.

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Acknowledgements

This is a part research accomplishment of the project “National Key Research and Development Program of China, No. 2018YFD1100803”.

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Authors and Affiliations

  1. College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China

    Maojun Wang & Wenyu Xu

  2. College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China

    Guangzhong Cao & Tao Liu

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  1. Maojun Wang
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  2. Wenyu Xu
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  3. Guangzhong Cao
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  4. Tao Liu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Maojun Wang, Wenyu Xu, Guangzhong Cao and Tao Liu. The first draft of the manuscript was written by Wenyu Xu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Maojun Wang.

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Wang, M., Xu, W., Cao, G. et al. Identification of rural courtyards’ utilization status using deep learning and machine learning methods on unmanned aerial vehicle images in north China. Build. Simul. 17, 799–818 (2024). https://doi.org/10.1007/s12273-023-1099-9

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  • DOI: https://doi.org/10.1007/s12273-023-1099-9

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