FROM-GLC Plus 3.0: Multi-modal 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 significant 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 sub-meter level) scale. Furthermore, the framework's ability to synthesize multi-modal 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.