Spot: A Natural Language Interface for Geospatial Searches in OSM
This addresses accessibility challenges for investigative journalists and fact-checkers using OSM, though it is an incremental application of existing NLP methods to a new domain.
The paper tackles the problem of OpenStreetMap's complexity by introducing Spot, a natural language interface that enables non-technical users to query geospatial data, achieving effective semantic mapping from natural language to OSM tags using a T5 transformer.
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.