GeoNLU: Bridging the gap between natural language and spatial data infrastructures

Abstract

Integrating natural language processing (NLP) techniques with spatial data infrastructures (SDIs) potentially revolutionize the way users interact with geospatial data. This article presents GeoNLU, a comprehensive framework aimed at bridging the gap between natural language and SDIs. GeoNLU aims to enable seamless interaction and querying of geospatial data through natural language, thereby enhancing accessibility and usability for a wide range of users. This article delves into the theoretical foundations, architectural design, key components, and potential applications of GeoNLU, highlighting its significance in improving geospatial data exploration, analysis, and decision-making.