Abstract
China’s rural population has declined markedly with the acceleration of urbanization and industrialization, but the area under rural homesteads has continued to expand. Proper rural land use and management require large-scale, efficient, and low-cost rural residential surveys; however, such surveys are time-consuming and difficult to accomplish. Unmanned aerial vehicle (UAV) technology coupled with a deep learning architecture and 3D modelling can provide a potential alternative to traditional surveys for gathering rural homestead information. In this study, a method to estimate the village-level homestead area, a 3D-based building height model (BHM), and the number of building floors based on UAV imagery and the U-net algorithm was developed, and the respective estimation accuracies were found to be 0.92, 0.99, and 0.89. This method is rapid and inexpensive compared to the traditional time-consuming and costly household surveys, and, thus, it is of great significance to the ongoing use and management of rural homestead information, especially with regards to the confirmation of homestead property rights in China. Further, the proposed combination of UAV imagery and U-net technology may have a broader application in rural household surveys, as it can provide more information for decision-makers to grasp the current state of the rural socio-economic environment.
Highlights
Massive rural–urban migration has accelerated the process of urbanization and industrialization in China in the last few decades
The objectives were: (1) to extract the spatial distribution of homesteads from Unmanned aerial vehicle (UAV) images, mainly relying on a pixel-based image classification using the U-net algorithm; (2) to develop and validate a building height model (BHM) to determine the number of floors and the floor area of rural buildings based on 3D modelling; and (3) to develop and test a village-level method to estimate homestead and floor areas in a rapid and low-cost manner, which is useful for rural surveys in China and other developing countries
The following quantitative indicators were used to evaluate performance in statistical analysis: overall accuracy, precision, recall, and F1 score. These indicators are presented as calculated true positives (TPs), false positives (FPs), true negatives (TNs), and false negatives (FNs)
Summary
Massive rural–urban migration has accelerated the process of urbanization and industrialization in China in the last few decades. In the estimation of the homestead and building floor areas at the village-level, it is still a challenge to explore a method applicable to rural China to achieve real-time image acquisition, pixel-based identification, and 3D modeling for rural buildings, one that provides a potential alternative to time-consuming and laborious household surveys. The objectives were: (1) to extract the spatial distribution of homesteads from UAV images, mainly relying on a pixel-based image classification using the U-net algorithm; (2) to develop and validate a building height model (BHM) to determine the number of floors and the floor area of rural buildings based on 3D modelling; and (3) to develop and test a village-level method to estimate homestead and floor areas in a rapid and low-cost manner, which is useful for rural surveys in China and other developing countries
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