Our work addresses the challenge of open vocabulary semantic segmentation for very high-resolution satellite imagery. This computer vision task goes beyond traditional semantic segmentation, which assigns predefined category labels to each image pixel. Instead, the open vocabulary approach enables the dynamic identification of any object or region through natural language queries, eliminating the constraints of fixed classification categories. This flexible approach represents a critical advancement in remote sensing applications, given the highly diverse scenes captured in satellite observations. We propose two novel solutions that build upon and enhance FC-CLIP, a state-of-the-art open vocabulary model originally designed for natural images. Our first solution, Remote FC-CLIP, integrates a remote sensing-specific CLIP model (Remote CLIP) into the baseline model's architecture, followed by fine-tuning on the OpenEarthMap (OEM) dataset.