Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction
Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formul...
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MDPI AG
2025-07-01
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| author | Bo Li Zhongfa Zhou Tianjun Wu Jiancheng Luo |
| author_facet | Bo Li Zhongfa Zhou Tianjun Wu Jiancheng Luo |
| author_sort | Bo Li |
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| description | Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological restoration projects, the ecological degradation of karst mountain areas in Southwest China has been significantly curbed. However, the research on the fine-grained land use mapping and quantitative characterization of spatial heterogeneity in karst mountain areas is still insufficient. This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. Hence, this paper proposes a novel methodology for land use mapping in karst mountain areas using very high resolution (VHR) remote sensing (RS) images. The innovation of this method lies in the introduction of strategies of geographical zoning and stratified object extraction. The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. Guanling and Zhenfeng counties in the Huajiang section of the Beipanjiang River Basin, China, are selected to conduct the experimental validation. The proposed method achieved notable accuracy metrics with an overall accuracy (<i>OA</i>) of 0.815 and a mean intersection over union (<i>mIoU</i>) of 0.688. Comparative analysis demonstrated the superior performance of advanced DL networks when augmented with priori knowledge in geographical zoning and stratified object extraction. The approach provides a robust mapping framework for generating fine-grained land use data in karst landscapes, which is beneficial for supporting academic research, governmental analysis, and related applications. |
| format | Article |
| id | doaj-art-c0165d8ce47b4e8f83bd956d1b7e357f |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-c0165d8ce47b4e8f83bd956d1b7e357f2025-08-20T02:47:09ZengMDPI AGRemote Sensing2072-42922025-07-011714236810.3390/rs17142368Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object ExtractionBo Li0Zhongfa Zhou1Tianjun Wu2Jiancheng Luo3School of Karst Science/School of Geography and Environment Science, Guizhou Normal University, Guiyang 550025, ChinaSchool of Karst Science/School of Geography and Environment Science, Guizhou Normal University, Guiyang 550025, ChinaSchool of Land Engineering, Chang’an University, Xi’an 710064, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKarst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological restoration projects, the ecological degradation of karst mountain areas in Southwest China has been significantly curbed. However, the research on the fine-grained land use mapping and quantitative characterization of spatial heterogeneity in karst mountain areas is still insufficient. This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. Hence, this paper proposes a novel methodology for land use mapping in karst mountain areas using very high resolution (VHR) remote sensing (RS) images. The innovation of this method lies in the introduction of strategies of geographical zoning and stratified object extraction. The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. Guanling and Zhenfeng counties in the Huajiang section of the Beipanjiang River Basin, China, are selected to conduct the experimental validation. The proposed method achieved notable accuracy metrics with an overall accuracy (<i>OA</i>) of 0.815 and a mean intersection over union (<i>mIoU</i>) of 0.688. Comparative analysis demonstrated the superior performance of advanced DL networks when augmented with priori knowledge in geographical zoning and stratified object extraction. The approach provides a robust mapping framework for generating fine-grained land use data in karst landscapes, which is beneficial for supporting academic research, governmental analysis, and related applications.https://www.mdpi.com/2072-4292/17/14/2368karst mountain areasfine-grained land use mappingvery high resolution remote sensing imagesgeographical zoningstratified object extraction |
| spellingShingle | Bo Li Zhongfa Zhou Tianjun Wu Jiancheng Luo Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction Remote Sensing karst mountain areas fine-grained land use mapping very high resolution remote sensing images geographical zoning stratified object extraction |
| title | Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction |
| title_full | Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction |
| title_fullStr | Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction |
| title_full_unstemmed | Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction |
| title_short | Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction |
| title_sort | fine grained land use remote sensing mapping in karst mountain areas using deep learning with geographical zoning and stratified object extraction |
| topic | karst mountain areas fine-grained land use mapping very high resolution remote sensing images geographical zoning stratified object extraction |
| url | https://www.mdpi.com/2072-4292/17/14/2368 |
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