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We present Text-to-OverpassQL, a task designed to facilitate a naturallanguage interface for querying geodata from OpenStreetMap (OSM). The OverpassQuery Language (OverpassQL) allows users to formulate complex database queriesand is widely adopted in the OSM ecosystem. Generating Overpass queries fromnatural language input serves multiple use-cases. It enables novice users toutilize OverpassQL without prior knowledge, assists experienced users withcrafting advanced queries, and enables tool-augmented large language models toaccess information stored in the OSM database. In order to assess theperformance of current sequence generation models on this task, we proposeOverpassNL, a dataset of 8,352 queries with corresponding natural languageinputs. We further introduce task specific evaluation metrics and ground theevaluation of the Text-to-OverpassQL task by executing the queries against theOSM database. We establish strong baselines by finetuning sequence-to-sequencemodels and adapting large language models with in-context examples. Thedetailed evaluation reveals strengths and weaknesses of the considered learningstrategies, laying the foundations for further research into theText-to-OverpassQL task.

Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap