A major difference between ground-level and satellite imagery of landscapes lies in their semantic granularity: ground-level images tend to offer details on objects and human activities, while satellite images provide broader geographic context but, typically, with coarser semantics. This study aims to leverage this complementary information by integrating fine-grained insights from a ground-level view into the analysis of satellite image data. To achieve this integration, we propose to align a satellite image representation with co-located geo-tagged ground-level image CLIP representations. This method focuses on enriching satellite image visual features by leveraging the inherent visual characteristics found in ground-level images as a reference in a contrastive manner, without relying on additional textual information to guide the learning process. We evaluate the quality of the learned representations on the EuroSAT benchmark in various few-shot settings.