This paper investigates cycling route preferences, with a focus on the cycling environment. To represent the cycling environment street-level images were used. A recently proposed model incorporates computer vision into a traditional discrete choice model to accommodate choice tasks involving numerical attributes and images. This computer vision-enriched discrete choice model (cv-dcm) was applied using a stated choice experiment, where respondents had to choose between two cycling routes. Each route was defined by three attributes, including commute time, number of traffic lights, and the cycling environment, the latter visualised using street-level images. While the cv-dcm relies on a neural network, making interpretability challenging, this study addressed this by collecting detailed cycling environment attributes. Results showed that the cycling environment was the most influential factor, with cyclists preferring green areas and separated cycling lanes. On average, cyclists were willing to take a 1.5-minute detour for a cycle trip of 11 minutes to use a separated cycling lane instead of a mixed-traffic road. These insights offer valuable insights for policymakers aiming to design cycling environments that align with cyclists’ preferences.