This paper presents a GeoAI pipeline for environmental monitoring and feature discovery using Large Language Models (LLMs) integrated with Prithvi and Clay geospatial embedding models. The system processes TIF satellite imagery to identify and mask land features in response to natural-language queries.
A human-in-the-loop verification mechanism enables the construction of an unsupervised learning workflow that continuously improves feature recognition and classification accuracy over time.