Utilizing prior information to enhance self-supervised aerial image analysis for extracting parking lot structures

Road network information (RNI) simplifies autonomous driving by providing strong priors about driving environments. Its usefulness has been demonstrated in the DARPA Urban Challenge. However, the need to manually generate RNI prevents us from fully exploiting its benefits. We envision an aerial image analysis system that automatically generates RNI for a route between two urban locations. As a step toward this goal, we present an algorithm that extracts the structure of a parking lot visible in an aerial image. We formulate this task as a problem of parking spot detection because extracting parking lot structures is closely related to detecting all of the parking spots. To minimize human intervention in use of aerial imagery, we devise a self-supervised learning algorithm that automatically obtains a set of canonical parking spot templates to learn the appearance of a parking lot and estimates the structure of the parking lot from the learned model. The data set extracted from a single image alone is too small to sufficiently learn an accurate parking spot model. To remedy this insufficient positive data problem, we utilize self-supervised parking spots obtained from other aerial images as prior information and a regularization technique to avoid an overfitting solution.