Towards accurate mapping of loess waterworn gully by integrating google earth imagery and DEM using deep learning
Accurate mapping of loess waterworn gully (LWG) is essential to further study gully erosion and geomorphological evolution for the Chinese Loess Plateau (CLP). Due to the vertical joint and collapsibility of loess, LWGs have the characteristics of zigzag and unique slope abruptness under synthetic a...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-03-01
|
| Series: | International Soil and Water Conservation Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S209563392300045X |
| _version_ | 1797226505762242560 |
|---|---|
| author | Rong Chen Yi Zhou Zetao Wang Ying Li Fan Li Feng Yang |
| author_facet | Rong Chen Yi Zhou Zetao Wang Ying Li Fan Li Feng Yang |
| author_sort | Rong Chen |
| collection | DOAJ |
| description | Accurate mapping of loess waterworn gully (LWG) is essential to further study gully erosion and geomorphological evolution for the Chinese Loess Plateau (CLP). Due to the vertical joint and collapsibility of loess, LWGs have the characteristics of zigzag and unique slope abruptness under synthetic action of hydraulic force and gravity. This forces existing LWG mapping methods to either focus on the improvement of mapping accuracy or center on the increase of mapping efficiency. However, simultaneously achieving accurate and efficient mapping of LWG is still in its infancy under complex topographic conditions. Here, we proposed a method that innovatively integrates the loess slope abruptness feature into an improved deep learning semantic segmentation framework for LWG mapping using 0.6 m Google imagery and 5 m DEM data. We selected four study areas representing typical loess landforms to test the performance of our method. The proposed method can achieve satisfactory mapping results, with the F1 score, mean Intersection-over-Union (mIoU), and overall accuracy of 90.5%, 85.3%, and 92.3%, respectively. In addition, the proposed model also showed significant accuracy improvement by inputting additional topographic information (especially the slope of slope). Compared with existing algorithms (Random forests, original DeepLabV3+, and Unet), the proposed approach in this study achieved a better accuracy-efficiency trade-off. Overall, the method can ensure high accuracy and efficiency of the LWG mapping for different loess landform types and can be extended to study various loess gully mapping and water and soil conservation. |
| first_indexed | 2024-03-08T22:47:04Z |
| format | Article |
| id | doaj.art-a9b71927e1ae4294b3d2cfee82eb6c72 |
| institution | Directory Open Access Journal |
| issn | 2095-6339 |
| language | English |
| last_indexed | 2024-04-24T14:25:59Z |
| publishDate | 2024-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Soil and Water Conservation Research |
| spelling | doaj.art-a9b71927e1ae4294b3d2cfee82eb6c722024-04-03T04:49:23ZengKeAi Communications Co., Ltd.International Soil and Water Conservation Research2095-63392024-03-011211328Towards accurate mapping of loess waterworn gully by integrating google earth imagery and DEM using deep learningRong Chen0Yi Zhou1Zetao Wang2Ying Li3Fan Li4Feng Yang5School of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, China; National Experiment and Teaching Demonstration Center for Geography, Xi'an, 710119, ChinaSchool of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, China; National Experiment and Teaching Demonstration Center for Geography, Xi'an, 710119, China; Corresponding author. School of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, China.SuperMap Software Co., Ltd., Beijing, 100015, ChinaSchool of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, China; National Experiment and Teaching Demonstration Center for Geography, Xi'an, 710119, ChinaSchool of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, China; National Experiment and Teaching Demonstration Center for Geography, Xi'an, 710119, ChinaSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, ChinaAccurate mapping of loess waterworn gully (LWG) is essential to further study gully erosion and geomorphological evolution for the Chinese Loess Plateau (CLP). Due to the vertical joint and collapsibility of loess, LWGs have the characteristics of zigzag and unique slope abruptness under synthetic action of hydraulic force and gravity. This forces existing LWG mapping methods to either focus on the improvement of mapping accuracy or center on the increase of mapping efficiency. However, simultaneously achieving accurate and efficient mapping of LWG is still in its infancy under complex topographic conditions. Here, we proposed a method that innovatively integrates the loess slope abruptness feature into an improved deep learning semantic segmentation framework for LWG mapping using 0.6 m Google imagery and 5 m DEM data. We selected four study areas representing typical loess landforms to test the performance of our method. The proposed method can achieve satisfactory mapping results, with the F1 score, mean Intersection-over-Union (mIoU), and overall accuracy of 90.5%, 85.3%, and 92.3%, respectively. In addition, the proposed model also showed significant accuracy improvement by inputting additional topographic information (especially the slope of slope). Compared with existing algorithms (Random forests, original DeepLabV3+, and Unet), the proposed approach in this study achieved a better accuracy-efficiency trade-off. Overall, the method can ensure high accuracy and efficiency of the LWG mapping for different loess landform types and can be extended to study various loess gully mapping and water and soil conservation.http://www.sciencedirect.com/science/article/pii/S209563392300045XLoess waterworn gullyTopographic informationMappingDeep learningSoil erosionLoess plateau |
| spellingShingle | Rong Chen Yi Zhou Zetao Wang Ying Li Fan Li Feng Yang Towards accurate mapping of loess waterworn gully by integrating google earth imagery and DEM using deep learning International Soil and Water Conservation Research Loess waterworn gully Topographic information Mapping Deep learning Soil erosion Loess plateau |
| title | Towards accurate mapping of loess waterworn gully by integrating google earth imagery and DEM using deep learning |
| title_full | Towards accurate mapping of loess waterworn gully by integrating google earth imagery and DEM using deep learning |
| title_fullStr | Towards accurate mapping of loess waterworn gully by integrating google earth imagery and DEM using deep learning |
| title_full_unstemmed | Towards accurate mapping of loess waterworn gully by integrating google earth imagery and DEM using deep learning |
| title_short | Towards accurate mapping of loess waterworn gully by integrating google earth imagery and DEM using deep learning |
| title_sort | towards accurate mapping of loess waterworn gully by integrating google earth imagery and dem using deep learning |
| topic | Loess waterworn gully Topographic information Mapping Deep learning Soil erosion Loess plateau |
| url | http://www.sciencedirect.com/science/article/pii/S209563392300045X |
| work_keys_str_mv | AT rongchen towardsaccuratemappingofloesswaterworngullybyintegratinggoogleearthimageryanddemusingdeeplearning AT yizhou towardsaccuratemappingofloesswaterworngullybyintegratinggoogleearthimageryanddemusingdeeplearning AT zetaowang towardsaccuratemappingofloesswaterworngullybyintegratinggoogleearthimageryanddemusingdeeplearning AT yingli towardsaccuratemappingofloesswaterworngullybyintegratinggoogleearthimageryanddemusingdeeplearning AT fanli towardsaccuratemappingofloesswaterworngullybyintegratinggoogleearthimageryanddemusingdeeplearning AT fengyang towardsaccuratemappingofloesswaterworngullybyintegratinggoogleearthimageryanddemusingdeeplearning |