Automatic license plate detection algorithms have significant industrial applications, such as traffic and parking management, as well as identity removal for privacy protection purposes. Nevertheless, a comprehensive large-scale dataset for supporting and benchmarking research in this area is currently not publicly available. This work aims to address this gap by introducing our augmentation to the Cityscapes and KITTI datasets, which are the two most extensively used datasets in autonomous driving field. Our proposed approach results in a collection of precise license plate annotations in 25 thousand images and 100 videos (20 thousand frames). We validate the necessity and importance of this new dataset (SATPlate) through experimental comparisons against existing datasets, with various modern object detection algorithms.