FROM-GLC Plus 3.0: Multimodal Land Change Mapping with SAM and Dense Surface Observations

Land cover mapping plays a critical role in monitoring land system changes. Despite advancements in remote sensing technologies, traditional satellite-based approaches are often constrained by cloud cover, coarse temporal resolution, and limitations in capturing fine-scale landscape dynamics, leading to gaps in continuous and real-time monitoring. Near-surface cameras offer a solution by providing high-frequency, ground-level observations that bridge temporal gaps and enhance spatial detail. Therefore, this study makes substantial contributions by pioneering the integration of near-surface camera observations with satellite imagery, addressing key challenges in imaging perspective differences and limited coverage of ground-based observations for enhanced land cover monitoring at a 30-m/10-m scale. A key innovation lies in leveraging near-surface cameras to reconstruct dense satellite data time series and capture daily land cover dynamics, addressing critical temporal gaps in traditional satellite-based approaches. The research further advances the field by implementing state-of-the-art deep learning techniques, particularly the Segment Anything Model (SAM), to achieve precise parcel-level delineation and reduce classification noise at a high-resolution (meter- and submeter level) scale. Furthermore, the framework’s ability to synthesize multimodal data sources (near-surface cameras, Sentinel-1/2, and high-resolution imagery) represents a methodological breakthrough in space and surface sensor integration for real-time land cover change detection, enabling time-sensitive applications and early warning systems for land system changes.

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Publication Details

Journal
Journal of Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.34133/remotesensing.0728
Citations
2
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
2.60
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FROM-GLC Plus 3.0: Multimodal Land Change Mapping with SAM and Dense Surface Observations

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2 citations
Journal of Remote Sensing
Remote Sensing and Land Use
2.60
article

FROM-GLC Plus 3.0: Multimodal Land Change Mapping with SAM and Dense Surface Observations

Hui Wu, Miaogen Shen, Zhicong Zhao, Zhentao Sun, Lingyun Liao, Yong Luo, Chaoyang Wu, Liheng Zhong, Jiatong Gu, Jinbang Peng, Ying Long, Tingting Li, Zhenrong Du, Weimin Song, Yonggan Zhao, Xinyue Li, Qiang Zhao, Yuanzheng Yang, Xiyu Li, Guanghui Lin, Yongguang Zhang, Pei Wang, Junqing Zheng, Xiaorui Ma, Dailiang Peng, Le Yu, Duojiweise
article en
2 citations

Abstract

Land cover mapping plays a critical role in monitoring land system changes. Despite advancements in remote sensing technologies, traditional satellite-based approaches are often constrained by cloud cover, coarse temporal resolution, and limitations in capturing fine-scale landscape dynamics, leading to gaps in continuous and real-time monitoring. Near-surface cameras offer a solution by providing high-frequency, ground-level observations that bridge temporal gaps and enhance spatial detail. Therefore, this study makes substantial contributions by pioneering the integration of near-surface camera observations with satellite imagery, addressing key challenges in imaging perspective differences and limited coverage of ground-based observations for enhanced land cover monitoring at a 30-m/10-m scale. A key innovation lies in leveraging near-surface cameras to reconstruct dense satellite data time series and capture daily land cover dynamics, addressing critical temporal gaps in traditional satellite-based approaches. The research further advances the field by implementing state-of-the-art deep learning techniques, particularly the Segment Anything Model (SAM), to achieve precise parcel-level delineation and reduce classification noise at a high-resolution (meter- and submeter level) scale. Furthermore, the framework’s ability to synthesize multimodal data sources (near-surface cameras, Sentinel-1/2, and high-resolution imagery) represents a methodological breakthrough in space and surface sensor integration for real-time land cover change detection, enabling time-sensitive applications and early warning systems for land system changes.

Journal of Remote SensingVol. 5
Nanjing Agricultural University (CN), Ministry of Natural Resources (CN), Beijing Normal University (CN), Dalian University of Technology (CN), China University of Geosciences (Beijing) (CN), National Institute of Biological Sciences, Beijing (CN), Dalian University (CN), Ministry of Education (IR), Yantai Institute of Coastal Zone Research (CN), Institute of Agricultural Resources and Regional Planning (CN), Ministry of Education (ME), Chinese Academy of Meteorological Sciences (CN), HUN-REN Centre for Ecological Research (HU), Aerospace Information Research Institute (CN), Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application (CN), Wuyistar (China) (CN), Nanning Normal University (CN), Institute of Atmospheric Physics (CN), Bureau of Geology and Mineral Exploration and Development of Guizhou Province (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN), Chinese Academy of Fishery Sciences (CN), Fujian Agriculture and Forestry University (CN), Beijing University of Civil Engineering and Architecture (CN), Tsinghua University (CN)
Climate action
Openalex Percentile: Top 10%
Remote Sensing and Land Use
2.60
DOIOpen Access PDFOpenAlex
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