# Spot: A Natural Language Interface for Geospatial Searches in OSM

arXiv: https://arxiv.org/abs/2311.08093 · HTML: https://mlsanity.com/paper/2311.08093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)

## Built on by (most cited first, 1 of 1)

- [SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalists](https://mlsanity.com/paper/2506.13188/) (2025)

## Abstract (arXiv)

Investigative journalists and fact-checkers have found OpenStreetMap (OSM) to be an invaluable resource for their work due to its extensive coverage and intricate details of various locations, which play a crucial role in investigating news scenes. Despite its value, OSM's complexity presents considerable accessibility and usability challenges, especially for those without a technical background. To address this, we introduce 'Spot', a user-friendly natural language interface for querying OSM data. Spot utilizes a semantic mapping from natural language to OSM tags, leveraging artificially generated sentence queries and a T5 transformer. This approach enables Spot to extract relevant information from user-input sentences and display candidate locations matching the descriptions on a map. To foster collaboration and future advancement, all code and generated data is available as an open-source repository.