Integrating Topographic Skeleton into Deep Learning for Terrain Reconstruction from GDEM and Google Earth Image
The topographic skeleton is the primary expression and intuitive understanding of topographic relief. This study integrated a topographic skeleton into deep learning for terrain reconstruction. Firstly, a topographic skeleton, such as valley, ridge, and gully lines, was extracted from a global digit...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/18/4490 |
| _version_ | 1827723862193733632 |
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| author | Kai Chen Chun Wang Mingyue Lu Wen Dai Jiaxin Fan Mengqi Li Shaohua Lei |
| author_facet | Kai Chen Chun Wang Mingyue Lu Wen Dai Jiaxin Fan Mengqi Li Shaohua Lei |
| author_sort | Kai Chen |
| collection | DOAJ |
| description | The topographic skeleton is the primary expression and intuitive understanding of topographic relief. This study integrated a topographic skeleton into deep learning for terrain reconstruction. Firstly, a topographic skeleton, such as valley, ridge, and gully lines, was extracted from a global digital elevation model (GDEM) and Google Earth Image (GEI). Then, the Conditional Generative Adversarial Network (CGAN) was used to learn the elevation sequence information between the topographic skeleton and high-precision 5 m DEMs. Thirdly, different combinations of topographic skeletons extracted from 5 m, 12.5 m, and 30 m DEMs and a 1 m GEI were compared for reconstructing 5 m DEMs. The results show the following: (1) from the perspective of the visual effect, the 5 m DEMs generated with the three combinations (5 m DEM + 1 m GEI, 12.5 m DEM + 1 m GEI, and 30 m DEM + 1 m GEI) were all similar to the original 5 m DEM (reference data), which provides a markedly increased level of terrain detail information when compared to the traditional interpolation methods; (2) from the perspective of elevation accuracy, the 5 m DEMs reconstructed by the three combinations have a high correlation (>0.9) with the reference data, while the vertical accuracy of the 12.5 m DEM + 1 m GEI combination is obviously higher than that of the 30 m DEM + 1 m GEI combination; and (3) from the perspective of topographic factors, the distribution trends of the reconstructed 5 m DEMs are all close to the reference data in terms of the extracted slope and aspect. This study enhances the quality of open-source DEMs and introduces innovative ideas for producing high-precision DEMs. Among the three combinations, we recommend the 12.5 m DEM + 1 m GEI combination for DEM reconstruction due to its relative high accuracy and open access. In regions where a field survey of high-precision DEMs is difficult, open-source DEMs combined with GEI can be used in high-precision DEM reconstruction. |
| first_indexed | 2024-03-10T22:05:47Z |
| format | Article |
| id | doaj.art-cb3c81ae5924430e8725fb5c7440edb2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T22:05:47Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-cb3c81ae5924430e8725fb5c7440edb22023-11-19T12:48:27ZengMDPI AGRemote Sensing2072-42922023-09-011518449010.3390/rs15184490Integrating Topographic Skeleton into Deep Learning for Terrain Reconstruction from GDEM and Google Earth ImageKai Chen0Chun Wang1Mingyue Lu2Wen Dai3Jiaxin Fan4Mengqi Li5Shaohua Lei6School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Physical Geographic Information in Anhui Province, Chuzhou 239000, ChinaSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaThe topographic skeleton is the primary expression and intuitive understanding of topographic relief. This study integrated a topographic skeleton into deep learning for terrain reconstruction. Firstly, a topographic skeleton, such as valley, ridge, and gully lines, was extracted from a global digital elevation model (GDEM) and Google Earth Image (GEI). Then, the Conditional Generative Adversarial Network (CGAN) was used to learn the elevation sequence information between the topographic skeleton and high-precision 5 m DEMs. Thirdly, different combinations of topographic skeletons extracted from 5 m, 12.5 m, and 30 m DEMs and a 1 m GEI were compared for reconstructing 5 m DEMs. The results show the following: (1) from the perspective of the visual effect, the 5 m DEMs generated with the three combinations (5 m DEM + 1 m GEI, 12.5 m DEM + 1 m GEI, and 30 m DEM + 1 m GEI) were all similar to the original 5 m DEM (reference data), which provides a markedly increased level of terrain detail information when compared to the traditional interpolation methods; (2) from the perspective of elevation accuracy, the 5 m DEMs reconstructed by the three combinations have a high correlation (>0.9) with the reference data, while the vertical accuracy of the 12.5 m DEM + 1 m GEI combination is obviously higher than that of the 30 m DEM + 1 m GEI combination; and (3) from the perspective of topographic factors, the distribution trends of the reconstructed 5 m DEMs are all close to the reference data in terms of the extracted slope and aspect. This study enhances the quality of open-source DEMs and introduces innovative ideas for producing high-precision DEMs. Among the three combinations, we recommend the 12.5 m DEM + 1 m GEI combination for DEM reconstruction due to its relative high accuracy and open access. In regions where a field survey of high-precision DEMs is difficult, open-source DEMs combined with GEI can be used in high-precision DEM reconstruction.https://www.mdpi.com/2072-4292/15/18/4490terrain reconstructionGDEMdeep learningGoogle Earth Imagethe Loess Plateau of China |
| spellingShingle | Kai Chen Chun Wang Mingyue Lu Wen Dai Jiaxin Fan Mengqi Li Shaohua Lei Integrating Topographic Skeleton into Deep Learning for Terrain Reconstruction from GDEM and Google Earth Image Remote Sensing terrain reconstruction GDEM deep learning Google Earth Image the Loess Plateau of China |
| title | Integrating Topographic Skeleton into Deep Learning for Terrain Reconstruction from GDEM and Google Earth Image |
| title_full | Integrating Topographic Skeleton into Deep Learning for Terrain Reconstruction from GDEM and Google Earth Image |
| title_fullStr | Integrating Topographic Skeleton into Deep Learning for Terrain Reconstruction from GDEM and Google Earth Image |
| title_full_unstemmed | Integrating Topographic Skeleton into Deep Learning for Terrain Reconstruction from GDEM and Google Earth Image |
| title_short | Integrating Topographic Skeleton into Deep Learning for Terrain Reconstruction from GDEM and Google Earth Image |
| title_sort | integrating topographic skeleton into deep learning for terrain reconstruction from gdem and google earth image |
| topic | terrain reconstruction GDEM deep learning Google Earth Image the Loess Plateau of China |
| url | https://www.mdpi.com/2072-4292/15/18/4490 |
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