A Large-Scale Multipurpose Benchmark Dataset and Real-Time Interpretation Platform Based on Chinese Rural Buildings
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 socioeconomic development in rural areas. However, the extensive scale, diversity, and wid...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10495095/ |
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| author | Weihuan Deng Weipan Xu Yaofu Huang Xun Li |
| author_facet | Weihuan Deng Weipan Xu Yaofu Huang Xun Li |
| author_sort | Weihuan Deng |
| collection | DOAJ |
| description | 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 socioeconomic 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 the mainland China. Moreover, a real-time 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. The 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 <italic>F</italic>1-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. |
| format | Article |
| id | doaj-art-5f02eb0598c24c3288bd7cf85b240a78 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-5f02eb0598c24c3288bd7cf85b240a782025-08-20T02:40:10ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117109141092810.1109/JSTARS.2024.338683010495095A Large-Scale Multipurpose Benchmark Dataset and Real-Time Interpretation Platform Based on Chinese Rural BuildingsWeihuan Deng0https://orcid.org/0000-0002-4146-1941Weipan Xu1https://orcid.org/0000-0002-2182-9382Yaofu Huang2https://orcid.org/0009-0001-2950-7695Xun Li3https://orcid.org/0000-0002-7190-0853School of Geography and Planning, Sun Yat-Sen University, Guangzhou, ChinaSchool of Geography and Planning, Sun Yat-Sen University, Guangzhou, ChinaSchool of Geography and Planning, Sun Yat-Sen University, Guangzhou, ChinaSchool of Geography and Planning, Sun Yat-Sen University, Guangzhou, ChinaAs 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 socioeconomic 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 the mainland China. Moreover, a real-time 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. The 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 <italic>F</italic>1-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.https://ieeexplore.ieee.org/document/10495095/Building footprintsdeep learninginstance segmentationremote sensingrural building dataset |
| spellingShingle | Weihuan Deng Weipan Xu Yaofu Huang Xun Li A Large-Scale Multipurpose Benchmark Dataset and Real-Time Interpretation Platform Based on Chinese Rural Buildings IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Building footprints deep learning instance segmentation remote sensing rural building dataset |
| title | A Large-Scale Multipurpose Benchmark Dataset and Real-Time Interpretation Platform Based on Chinese Rural Buildings |
| title_full | A Large-Scale Multipurpose Benchmark Dataset and Real-Time Interpretation Platform Based on Chinese Rural Buildings |
| title_fullStr | A Large-Scale Multipurpose Benchmark Dataset and Real-Time Interpretation Platform Based on Chinese Rural Buildings |
| title_full_unstemmed | A Large-Scale Multipurpose Benchmark Dataset and Real-Time Interpretation Platform Based on Chinese Rural Buildings |
| title_short | A Large-Scale Multipurpose Benchmark Dataset and Real-Time Interpretation Platform Based on Chinese Rural Buildings |
| title_sort | large scale multipurpose benchmark dataset and real time interpretation platform based on chinese rural buildings |
| topic | Building footprints deep learning instance segmentation remote sensing rural building dataset |
| url | https://ieeexplore.ieee.org/document/10495095/ |
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