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Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the \u201cbeautiful countryside\u201d construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 \u00d7 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide.<\/jats:p>","DOI":"10.3390\/rs14205175","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"5175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Building Extraction and Floor Area Estimation at the Village Level in Rural China Via a Comprehensive Method Integrating UAV Photogrammetry and the Novel EDSANet"],"prefix":"10.3390","volume":"14","author":[{"given":"Jie","family":"Zhou","sequence":"first","affiliation":[{"name":"Institute of Geology, China Earthquake Administration, Beijing 100029, China"},{"name":"Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3041-3557","authenticated-orcid":false,"given":"Yaohui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"},{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"},{"name":"School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaozhong","family":"Nie","sequence":"additional","affiliation":[{"name":"Institute of Geology, China Earthquake Administration, Beijing 100029, China"},{"name":"Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-3227","authenticated-orcid":false,"given":"Xiaoxian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lutz","family":"Gross","sequence":"additional","affiliation":[{"name":"School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101576","DOI":"10.1016\/j.ijdrr.2020.101576","article-title":"Seismic vulnerability comparison between rural Weinan and other rural areas in Western China","volume":"48","author":"Li","year":"2020","journal-title":"Int. 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