Fine-tuning the Contrastive Language-Image Pre-Training (CLIP) model with the RSICD dataset improves data curation for geospatial tasks by enhancing semantic search, multilingual annotations, and location-based data processing accuracy and efficiency. Geo-spatial embeddings are crucial for various applications such as GIS, location-based recommendation systems, urban planning, environmental monitoring, and disaster response, but generating accurate embeddings from heterogeneous data sources poses significant challenges. By fine-tuning VLMs like CLIP to produce more accurate and semantically rich geospatial embeddings, the importance of fine-tuning VLMs in data curation is emphasized through aspects such as semantic understanding, adaptability to domain-specific requirements, improved data accuracy, and enhanced contextual understanding. Fine-tuning CLIP with RSICD enables efficient search, consistent labeling, multilingual support, and domain-specific expertise, paving the way for smarter, more accessible datasets.