State departments of transportation often maintain extensive “video logs” of their roadways that include signs, lane markings, as well as non-image-based information such as grade, curvature, etc. In this work we use the Roadway Information Database (RID), developed for the Second Strategic Highway Research Program, as a surrogate for a video log to design and test algorithms to detect rumble strips in the roadway images. Rumble strips are grooved patterns at the lane extremities designed to produce an audible queue to drivers who are in danger of lane departure. The RID contains 6,203,576 images of roads in six locations across the United States with extensive ground truth information and measurements, but the rumble strip measurements (length and spacing) were not recorded. We use an image correction process along with automated feature extraction and convolutional neural networks to detect rumble strip locations and measure their length and pitch. Based on independent measurements, we estimate our true positive rate to be 93% and false positive rate to be 10% with errors in length and spacing on the order of 0.09 meters RMS and 0.04 meters RMS. Our results illustrate the feasibility of this approach to add value to video logs after initial capture as well as identify potential methods for autonomous navigation.