Collecting up-to-date roadway geometry data is essential for transportation agencies so they can undertake planning, maintenance, design, and rehabilitation of roadways. Collection methods can be categorized into two distinct groups: land-based methods (e.g., field inventories, mobile mapping, and image logging); and aerial-based methods (e.g., satellite imagery, drones, and laser scanning). Because using land-based methods for thousands of miles of highways is tedious, costly, and risky for crew members, there is a need to develop better methodologies to collect these data faster, and more safely and cheaply. The increasing availability of high-resolution images and recent advances in computer vision and object detection techniques have enabled the automated extraction of roadway geometry features. This novel study proposes a computer vision-based methodology to detect school zone pavement markings from high-resolution aerial images and determine school zones on Florida’s public roadways. This is critical information for transportation agencies, and they use it for a variety of different purposes: identifying those markings that are old and invisible; comparing the school zone locations with other geometric features such as crosswalks; and analyzing crashes that occur around the zones. Compared with the ground truth data obtained for Leon County, Florida, 94% accuracy was observed at the 90% confidence level. The model was then used to detect school zones in Orange County, Florida, and approximately 500 school zone markings were identified automatically. The road geometry data extracted can be integrated with crash and traffic data to advise policymakers and roadway users.

Advancements in computer vision are rapidly revolutionizing the way traffic agencies gather roadway geometry data, leading to significant savings in both time and money. Utilizing aerial and satellite imagery for data collection proves to be more cost-effective, more accurate, and safer compared to traditional field observations, considering factors such as equipment cost, crew safety, and data collection efficiency. Consequently, there is a pressing need to develop more efficient methodologies for promptly, safely, and economically acquiring roadway geometry data. While image processing has previously been regarded as a time-consuming and error-prone approach for capturing these data, recent developments in computing power and image recognition techniques have opened up new avenues for accurately detecting and mapping various roadway features from a wide range of imagery data sources. This research introduces a novel approach combining image processing with a YOLO-based methodology to detect turning lane pavement markings from high-resolution aerial images, specifically focusing on Florida’s public roadways. Upon comparison with ground truth data from Leon County, Florida, the developed model achieved an average accuracy of 87% at a 25% confidence threshold for detected features. Implementation of the model in Leon County identified approximately 3026 left turn, 1210 right turn, and 200 center lane features automatically. This methodology holds paramount significance for transportation agencies in facilitating tasks such as identifying deteriorated markings, comparing turning lane positions with other roadway features like crosswalks, and analyzing intersection-related accidents. The extracted roadway geometry data can also be seamlessly integrated with crash and traffic data, providing crucial insights for policymakers and road users.

The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent improvements in computational power and image recognition techniques, there are now reliable methods to identify and map various roadway elements from multiple imagery sources. Notably, comprehensive geospatial data for pedestrian and bicycle lanes are still lacking across many state and local roadways, including those in the State of Florida, despite the essential role this information plays in optimizing traffic efficiency and reducing crashes. Developing fast, efficient methods to gather this data are essential for transportation agencies as they also support objectives like identifying outdated or obscured markings, analyzing pedestrian and bicycle lane placements relative to crosswalks, turning lanes, and school zones, and assessing crash patterns in the associated areas. This study introduces an innovative approach using deep neural network models in image processing and computer vision to detect and extract pedestrian and bicycle lane features from very high-resolution aerial imagery, with a focus on public roadways in Florida. Using YOLOv5 and MTRE-based deep learning models, this study extracts and segments bicycle and pedestrian features from high-resolution aerial images, creating a geospatial inventory of these roadway features. Detected features were post-processed and compared with ground truth data to evaluate performance. When tested against ground truth data from Leon County, Florida, the models demonstrated accuracy rates of 73% for pedestrian lanes and 89% for bicycle lanes. This initiative is vital for transportation agencies, enhancing infrastructure management by enabling timely identification of aging or obscured lane markings, which are crucial for maintaining safe transportation networks.