Computer vision-based model for detecting turning lane features on Florida's public roadways from aerial images

Efficient collection of roadway geometry data is crucial for effective transportation planning, maintenance, and design. Current methods involve land-based techniques like field inventory and aerial-based methods such as satellite imagery. However, land-based approaches are labor-intensive and costly, prompting the need for more efficient methodologies. Consequently, there exists a pressing need to develop more efficient methodologies for acquiring this data promptly, safely, and economically. This study proposes a computer vision-based approach to detect turning lane markings from aerial images in Florida. The method aims to identify aged or faded markings, compare lane locations with other features, and analyze intersection crashes. Validation in Leon County achieved 80.4% accuracy, detecting over 13,800 turning lane features in Duval County, Florida. This data integration offers valuable insights for policymakers and road users, highlighting the significance of automated extraction methods in transportation planning and safety.