Optimization of Modelling Population Density Estimation Based on Impervious Surfaces

Zang, Jinyu; Zhang, Ting; Chen, Longqian; Li, Long; Liu, Weiqiang; Yuan, Lina; Liu, Ruiyang; Wang, Zhiqiang; Yu, Ziqi; Jia, Wang

doi:10.3390/land10080791

Cited by 6 publications

(1 citation statement)

References 50 publications

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“…However, most rural building information is currently acquired through statistical methods such as field surveys and mapping (Boo et al, 2022). Due to the vast territory and complex spatial pattern in the Chinese rural countryside, conducting a national building census is laborious and time-consuming (Zang et al, 2021). Moreover, it is clearly not feasible to visually interpret rural buildings from remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

A Large-Scale Multipurpose Benchmark Dataset and Real-Time Interpretation Platform Based on Chinese Rural Buildings

Deng,

Xu,

Huang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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As urbanization accelerates, the evolving dynamics of village growth and decline have garnered widespread attention. Rural housing, as the most significant asset in villages, serves as the primary indicator of socio-economic development in rural areas. However, the extensive scale, diversity, and widespread distribution of villages make conducting a nationwide census of rural buildings a notably costly and time-intensive endeavor. Although deep learning techniques have been successfully applied by numerous researchers to map building footprints, the majority of this work is concentrated in urban areas, leaving large-scale datasets for rural buildings notably lacking. In this article, an exhaustive database of rural architecture has been established, featuring diverse rural building annotations from the majority of provinces in mainland China. Moreover, a realtime online platform for remote sensing image interpretation, integrating instance segmentation and boundary regularization, has been developed to streamline the extraction of building footprints from high-resolution imagery. Experimental results from predicting 43,992 rural building instances nationwide demonstrated that 33,210 were accurately identified, achieving a precision of 0.776, a recall of 0.755, and an F1 score of 0.765. Building upon this work, the maps of rural building areas and quantity are produced to clearly demonstrate the distribution of rural houses in parts of China. These data products can serve as vital supplements to public data products such as nighttime light data, land cover maps, national statistical yearbooks, and road network data, particularly in the field of rural studies.

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Section: Introductionmentioning

confidence: 99%