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.

Identifying changes in pavement markings has become crucial for infrastructure monitoring, maintenance, development, traffic management, and safety. Automated extraction of the roadway geometry is critical in helping with this, given the increasing availability of high-resolution images and advancements in computer vision and object detection. Specifically, because of the substantial volume of satellite and high-resolution aerial images captured at different time instances, change detection has become a viable solution. In this study, an automated framework is developed to detect changes in crosswalks in Orange, Osceola, and Seminole counties in Florida, utilizing data extracted from high-resolution images obtained at various time intervals. Specifically, for Orange County, crosswalk changes between 2019 and 2021 were manually extracted, verified, and categorized as either new or modified crosswalks. For Seminole County, the developed model was used to automatically extract crosswalk changes between 2018 and 2021, while for Osceola County, changes between 2019 and 2020 were extracted. Findings indicate that Orange County witnessed approximately 2,094 crosswalk changes, with 314 occurring on state roads. In Seminole and Osceola counties, on the other hand, 1,040 and 1,402 crosswalk changes were observed on both local and state roads, respectively. Among these, 340 and 344 were identified on state roads in Seminole and Osceola, respectively. Spatiotemporal changes observed in crosswalks can be utilized to regularly update the existing crosswalk inventories, which is essential for agencies engaged in traffic and safety studies. Data extracted from these crosswalk changes can be combined with traffic and crash data to provide valuable insights to policymakers.

Natural disasters like hurricanes can severely disrupt transportation systems, leading to roadway closures and limiting accessibility, which has extreme economic, social, and sustainability implications. This study investigates the impact of hurricanes Ian and Idalia on roadway accessibility in Florida using machine learning techniques. High-resolution satellite imagery, combined with demographic and hurricane-related roadway data, was used to assess the extent of road closures in southeast Florida (Hurricane Ian) and northwest Florida (Hurricane Idalia). The model detected roadway segments as open, partially closed, or fully closed, achieving an overall accuracy of 89%, with confidence levels of 92% and 85% for the two hurricanes, respectively. The results showed that heavily populated coastal regions experienced the most significant disruptions, with more extensive closures and reduced accessibility. This research demonstrates how machine learning can enhance disaster recovery efforts by identifying critical infrastructure in need of immediate attention, supporting sustainable resilience in post-hurricane recovery. The findings suggest that integrating such methods into disaster planning can improve the efficiency and sustainability of recovery operations, helping to allocate resources more effectively in future disaster events.