# SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalists

arXiv: https://arxiv.org/abs/2506.13188 · HTML: https://mlsanity.com/paper/2506.13188/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Builds on (most cited first, 14 of 14)

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- [Large Language Model Enhanced Text-to-SQL Generation: A Survey](https://mlsanity.com/paper/2410.06011/) (2024)
- [Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap](https://mlsanity.com/paper/2308.16060/) (2023)
- [Spot: A Natural Language Interface for Geospatial Searches in OSM](https://mlsanity.com/paper/2311.08093/) (2023)

## Abstract (arXiv)

OpenStreetMap (OSM) is a vital resource for investigative journalists doing geolocation verification. However, existing tools to query OSM data such as Overpass Turbo require familiarity with complex query languages, creating barriers for non-technical users. We present SPOT, an open source natural language interface that makes OSM's rich, tag-based geographic data more accessible through intuitive scene descriptions. SPOT interprets user inputs as structured representations of geospatial object configurations using fine-tuned Large Language Models (LLMs), with results being displayed in an interactive map interface. While more general geospatial search tasks are conceivable, SPOT is specifically designed for use in investigative journalism, addressing real-world challenges such as hallucinations in model output, inconsistencies in OSM tagging, and the noisy nature of user input. It combines a novel synthetic data pipeline with a semantic bundling system to enable robust, accurate query generation. To our knowledge, SPOT is the first system to achieve reliable natural language access to OSM data at this level of accuracy. By lowering the technical barrier to geolocation verification, SPOT contributes a practical tool to the broader efforts to support fact-checking and combat disinformation.