  We present Text-to-OverpassQL, a task designed to facilitate a natural
language interface for querying geodata from OpenStreetMap (OSM). The Overpass
Query Language (OverpassQL) allows users to formulate complex database queries
and is widely adopted in the OSM ecosystem. Generating Overpass queries from
natural language input serves multiple use-cases. It enables novice users to
utilize OverpassQL without prior knowledge, assists experienced users with
crafting advanced queries, and enables tool-augmented large language models to
access information stored in the OSM database. In order to assess the
performance of current sequence generation models on this task, we propose
OverpassNL, a dataset of 8,352 queries with corresponding natural language
inputs. We further introduce task specific evaluation metrics and ground the
evaluation of the Text-to-OverpassQL task by executing the queries against the
OSM database. We establish strong baselines by finetuning sequence-to-sequence
models and adapting large language models with in-context examples. The
detailed evaluation reveals strengths and weaknesses of the considered learning
strategies, laying the foundations for further research into the
Text-to-OverpassQL task.
