We have developed an automated, low-cost, and generalizable approach using Google Street View images and deep learning techniques to evaluate bus stop amenities. Leveraging the latest YOLOv8 model, transfer learning, and a dynamic prediction algorithm, our approach achieves efficient detection of bus stop amenities (e.g., shelters and benches) with high accuracy and precision. Scalability and transferability tests further suggest that highly accurate feature detection results can be achieved through model fine-tuning on a small sample of local data.
Key words: Computer Vision, transit, Google Street View Image, Bus Stop Amenities
Dai, Y., Liu, L., Wang, K., L., M., Yan, X. Using Deep Learning and Google Street View Images to Assess Bus Stop Amenities. Download Preprint.