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.

翻译：摘要：调查记者和事实核查人员发现，OpenStreetMap（OSM）因其对各种地点广泛而细致的覆盖（这在调查新闻场景中起着关键作用）而成为他们工作中不可或缺的资源。尽管OSM价值巨大，但其复杂性带来了显著的可访问性和可用性挑战，尤其对于缺乏技术背景的用户而言。为解决这一问题，我们引入了'Spot'——一个用户友好的自然语言接口，用于查询OSM数据。Spot利用从自然语言到OSM标签的语义映射，借助人工生成的句子查询和T5 Transformer模型实现这一功能。该方法使Spot能够从用户输入的句子中提取相关信息，并在地图上显示与描述相匹配的候选位置。为促进协作和未来发展，所有代码和生成的数据均以开源仓库形式提供。