Retrogressive thaw slumps (RTSs) are key indicators of permafrost degradation in Arctic regions. Yet, their detection remains challenging due to spectral similarity with surrounding terrain and the limited generalization of conventional deep learning approaches. This study presents a Geo-Foundation framework that integrates pretrained Clay embeddings with high-resolution PlanetScope multispectral imagery, spectral indices, and ArcticDEM data for RTS detection in the Northwest Territory (NWT), Canada. The proposed dual-branch architecture combines high-level geospatial representations with physically meaningful environmental features to improve segmentation performance. The model achieved an F1-score of 0.83 and a mean Intersection-over-Union (mIoU) of 0.75 on the validation dataset. Analysis of patch size indicates that intermediate spatial context provides optimal performance, while feature importance results highlight the dominant role of vegetation-sensitive spectral bands and indices. Qualitative evaluation further confirms accurate boundary delineation and spatial consistency across diverse terrain conditions. The results demonstrate that Geo-Foundation models enhance detection accuracy, reduce dependence on large labeled datasets, and improve generalization across heterogeneous Arctic landscapes. This approach provides a scalable and efficient solution for monitoring permafrost-related disturbances under a changing climate.