<p>There is growing interest for urban areas to develop road infrastructure that encourages cycling as a sustainable transport mode. However, comprehensive data on cycling infrastructure is lacking, especially for intersections. This is a crucial barrier for studying the impact of infrastructure modifications (e.g., on encouraging cycling demand or crash risks). Existing cycling infrastructure data suffers from at least one of the following deficiencies: (1) fragmentation along administrative borders, (2) link-oriented data without information on intersections, and (3) missing information on previous infrastructure changes. The contribution of this paper is threefold. First, an object detection method is utilised on aerial imagery to generate a dataset that addresses all three previously mentioned issues. For this purpose, a YOLOv11 model (a common deep learning architecture for object detection) is trained to detect ten different cycling-relevant infrastructure features, using Switzerland as a case study. Second, it is demonstrated that the method is valid by comparing a subset of the resulting dataset to an external municipality-level dataset. Third, the overall historical development of cycling-specific infrastructure in the ten largest Swiss agglomerations is presented. These results provide the basis for future analyses that depend on the knowledge of cycling infrastructure presence.</p>