A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data
Building height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China’s Gaofen-7 satel...
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MDPI AG
2024-09-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/18/6076 |
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| author | Mingbo Liu Ping Wang Kailong Hu Changjun Gu Shengyue Jin Lu Chen |
| author_facet | Mingbo Liu Ping Wang Kailong Hu Changjun Gu Shengyue Jin Lu Chen |
| author_sort | Mingbo Liu |
| collection | DOAJ |
| description | Building height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China’s Gaofen-7 satellite (GF-7). In this study, we developed a method combining photogrammetry and deep learning to extract building height using GF-7 data in the rural area of Pingquan in northern China. The deep learning model DELaMa was proposed for digital surface model (DSM) editing based on the Large Mask Inpainting (LaMa) architecture. It not only preserves topographic details but also reasonably predicts the topography inside the building mask. The percentile value of the normalized digital surface model (nDSM) in the building footprint was taken as the building height. The extracted building heights in the study area are highly consistent with the reference building heights measured from the ICESat-2 LiDAR point cloud, with an R<sup>2</sup> of 0.83, an MAE of 1.81 m and an RMSE of 2.13 m for all validation buildings. Overall, the proposed method in this paper helps to promote the use of satellite data in large-scale building height surveys, especially in rural areas. |
| format | Article |
| id | doaj-art-8b40ffa1839248ceb9a9bd40db8258af |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
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| series | Sensors |
| spelling | doaj-art-8b40ffa1839248ceb9a9bd40db8258af2025-08-20T01:55:51ZengMDPI AGSensors1424-82202024-09-012418607610.3390/s24186076A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 DataMingbo Liu0Ping Wang1Kailong Hu2Changjun Gu3Shengyue Jin4Lu Chen5National Disaster Reduction Center of China, Ministry of Emergency Management of the People’s Republic of China, Beijing 100124, ChinaNational Disaster Reduction Center of China, Ministry of Emergency Management of the People’s Republic of China, Beijing 100124, ChinaNational Disaster Reduction Center of China, Ministry of Emergency Management of the People’s Republic of China, Beijing 100124, ChinaNational Disaster Reduction Center of China, Ministry of Emergency Management of the People’s Republic of China, Beijing 100124, ChinaNational Disaster Reduction Center of China, Ministry of Emergency Management of the People’s Republic of China, Beijing 100124, ChinaNational Disaster Reduction Center of China, Ministry of Emergency Management of the People’s Republic of China, Beijing 100124, ChinaBuilding height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China’s Gaofen-7 satellite (GF-7). In this study, we developed a method combining photogrammetry and deep learning to extract building height using GF-7 data in the rural area of Pingquan in northern China. The deep learning model DELaMa was proposed for digital surface model (DSM) editing based on the Large Mask Inpainting (LaMa) architecture. It not only preserves topographic details but also reasonably predicts the topography inside the building mask. The percentile value of the normalized digital surface model (nDSM) in the building footprint was taken as the building height. The extracted building heights in the study area are highly consistent with the reference building heights measured from the ICESat-2 LiDAR point cloud, with an R<sup>2</sup> of 0.83, an MAE of 1.81 m and an RMSE of 2.13 m for all validation buildings. Overall, the proposed method in this paper helps to promote the use of satellite data in large-scale building height surveys, especially in rural areas.https://www.mdpi.com/1424-8220/24/18/6076GF-7 imagebuilding heightrural areasdeep learning |
| spellingShingle | Mingbo Liu Ping Wang Kailong Hu Changjun Gu Shengyue Jin Lu Chen A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data Sensors GF-7 image building height rural areas deep learning |
| title | A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data |
| title_full | A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data |
| title_fullStr | A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data |
| title_full_unstemmed | A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data |
| title_short | A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data |
| title_sort | method for extracting high resolution building height information in rural areas using gf 7 data |
| topic | GF-7 image building height rural areas deep learning |
| url | https://www.mdpi.com/1424-8220/24/18/6076 |
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