# MaaSDB: Spatial Databases in the Era of Large Language Models (Vision Paper)

arXiv: https://arxiv.org/abs/2309.17072 · HTML: https://mlsanity.com/paper/2309.17072/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [Using LLMs for Analyzing AIS Data](https://mlsanity.com/paper/2504.07557/) (2025)
- [Navigate Complex Physical Worlds via Geometrically Constrained LLM](https://mlsanity.com/paper/2410.17529/) (2024)
- [Graph-Enhanced Large Language Models for Spatial Search](https://mlsanity.com/paper/2606.22909/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are advancing rapidly. Such models have demonstrated strong capabilities in learning from large-scale (unstructured) text data and answering user queries. Users do not need to be experts in structured query languages to interact with systems built upon such models. This provides great opportunities to reduce the barrier of information retrieval for the general public. By introducing LLMs into spatial data management, we envisage an LLM-based spatial database system to learn from both structured and unstructured spatial data. Such a system will offer seamless access to spatial knowledge for the users, thus benefiting individuals, business, and government policy makers alike.