Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment

Cloud removal (CR) is essential for optical remote sensing,serving as a prerequisite for reliable downstream interpretation, suchas semantic segmentation and change detection. However, existing CRapproaches often prioritize visual realism while overlooking their im-pact on subsequent analytical tasks, leading to semantic drift and de-graded downstream performance. To address this issue, we propose Geo-Anchored Cloud Removal (GACR), a unified framework that jointlyensures faithful reconstruction and robust interpretability. At its core,GACR incorporates Observation-Anchored Residual Flow (OAR-Flow),which reformulates CR as a physically grounded residual inversion pro-cess. By anchoring the generative trajectory to the cloudy observationrather than pure noise, OAR-Flow enables fast, stable, and faithful re-construction. To further preserve semantic structures critical for down-stream interpretation, GACR integrates Geo-Contextual Prior Align-ment (GCPA) to constrain the reconstruction within a semantic manifoldinduced by a Vision Foundation Model (VFM). Consequently, GACRstrictly maintains the spatial-semantic integrity of complex landscapes.Extensive experiments across six CR datasets and twelve downstreamtasks demonstrate that GACR produces superior reconstruction qual-ity while consistently improving downstream task accuracy. The code isavailable at https://github.com/wzy6055/GACR.