数字农科院2.0

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

文献类型： 外文期刊

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

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期刊名称： JOURNAL OF REMOTE SENSING

ISSN：

年卷期： 2025 年 5 卷

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收录情况： EI（2025版） ; ; 北大核心（2023版） ; ; CSCD（2025-2026年度） ; ; 科技核心（2024版） ; ; 农林核心（2024版）ESCI（2025版）

摘要： 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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