A Dual-Mode Conversational GIS for Proximity and Image-Inferred Category-Based Routing using CLIP
Abstract
This paper presents the design and implementation of a dual-mode conversational Geographic Information System (GIS) routing assistant integrated into the PrimeMap web platform. The system employs a chatbot interface to guide users through two distinct route-planning modes: Closest, which selects destinations based on nearest spatial proximity using sequential location calculations, and Theme, which builds routes according to place categories. In Theme mode, users can either manually select categories or upload an image for AI-assisted classification using OpenAI’s CLIP model. The image-driven approach automatically determines the relevant category by matching detected semantic labels to pre-defined groups in the GIS database, enabling a seamless visual search capability. The conversational interface, built with BotUI and integrated into a Leaflet-based map UI, supports dynamic user input, immediate visual feedback, and flexible route building. The backend, implemented in Spring Boot, manages category/group/place logic, image processing requests, and route computation using the Haversine formula for nearest location detection. The current implementation prioritizes simplicity and user experience, while planned future work includes more complex multi-criteria ranking (e.g., cosine similarity, opening hours, ratings), optional GPS-based starting locations, advanced search filters, and richer AI-assisted matching. Experimental evaluation demonstrated 92 % classification accuracy on a balanced 50-image test set, with average route-generation latency below one second and consistent thematic match performance across ten categories. This dual-mode chatbot demonstrates how conversational GIS can bridge the gap between traditional map interfaces and intelligent, user-adaptive routing, offering potential applications in tourism, urban mobility, and location-based services.References
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