Retrieval-Augmented Generation for the semantic exploration of geospatial datasets and metadata

Teacher: Giovanna Venuti

Supervisor: Ing. Gianluca Murdaca – MindEarth

Description: The project aims to build a prototype system that enables natural-language querying of a small knowledge base composed of geospatial dataset descriptions, metadata, and technical documentation. The student will implement a simple Retrieval-Augmented Generation workflow to support semantic search and question answering over geospatial information resources.

Difficult level: Medium

Requirements: Python, basic NLP concepts, interest in geospatial data and metadata

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