Geo-R2LLM

Recent Artificial Intelligence (AI) research has given rise to a paradigm shift brought by Large Language Models (LLMs). Though LLMs have taken mostly their root in Natural Language Processing (NLP), it is well-known today that zero-shot and few-shot transfer learning methodologies make their deployment possible beyond the NLP field, achieving impressive performance on a significant range of domains and downstream tasks.  However, the deployment of LLMs in geographic information systems is still in early stages.

   The Geo-R2LLM project moves towards a novel paradigm for building knowledgeable and multimodal geographic LLMs by rethinking LLMs generation mode with retrieval and reasoning over multiple multimodal external knowledge sources to ground the prediction. The improved multimodal geographic LLMs will be integrated in a geospatio-temporal artificial intelligence (GeoAI) system prototype and evaluated on a pilot related to context-aware navigation system in a complex urban environment. Navigation services can be considered as one of the most critical and widely adopted location-based services in modern societies, hence the project has potentially strong impact also outside of academia.

   The aims to advance multiple disciplines spanning GeoAI, spatia and spatio-temporal reasoning, information retrieval, and natural language understanding, laying the groundwork for more effective AI platforms for various domains that relate to geography and geographical information science.