Workshop Name：1st Large Language Models for Spatial-rich Data Management (LLM+Spatial)

Address：London, United Kingdom - September 5, afternoon, 2025

Website：https://llmspatial.github.io/2025/

Overview

The importance of spatio-temporal data has increased significantly in various scientific fields, such as climate research, biodiversity, and the social sciences, primarily due to improvements in data collection and accessibility. Despite the opportunities for new scientific insight, researchers often face the challenge of inadequate tools and interfaces for managing, integrating, and analyzing spatio-temporal data. Recently, the emergent abilities of LLMs represent a pivotal point that is to significantly affect the academic and industrial communities. The vast amount of knowledge in spatial-rich data is not used to train and tune LLMs, and, spatio-temporal databases are not able to access and operate on the facts contained in the LLMs. This workshop aims to provide new insight into techniques from spatial-rich data and LLMs to improve advances in spatial-rich data management and predictive models.

Call for Papers

The goal is to advance the understanding of how LLMs and spatial-rich data management can cooperatively contribute to novel data science solutions. Topics of interest include, but are not limited to:

· Spatial-rich Data Foundation Model

· Enhance LLMs by Spatial-rich Data

· Spatial-rich Data Quality, Anomaly Detection, and Imputation with LLMs

· Retrieval-augmented Models for Geospatial Applications

· NL2SQL for Spatio-temporal Data

· Spatial and Temporal-spatial Contextual Reasoning with LLMs

· Embedding Learning for Geospatial Data with LLMs

· Fine-tuning LLMs on Domain-specific Geospatial Data

· Benchmarking of LLMs + Spatio-temporal Databases

· Optimizing Spatio-temporal Databases with LLMs

· Cases Studies and Applications of LLMs + Spatial-rich Data

· Visions for LLMs + Spatio-temporal Databases