Natural Language Processing in Geographic Information Systems

The integration of Geographic Information Systems (GIS) with Natural Language Processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient contextawareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geodescriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies.