Work

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

Year: 2025

Type: article

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.

Source: Journal of Remote Sensing

DOI: 10.34133/remotesensing.0728