Metric localization of automated vehicles using exteroceptive sensors involves finding reliable spatial features in both the sensor data and the map. In real world scenarios, methods have to deal with unknown and changing environments and noisy sensor measurements, making feature selection considerably harder. If the map is created using a different sensor modality, localization methods also have to deal with the characteristics of the available sensor. Machine learning methods promise a solution to these problems by extracting the same features from maps created from different sensors. In this work, we compare an approach for learning based cross modality localization with a classical method on different types of maps. Furthermore, we enhance the learned model by estimating its uncertainty directly from the measurement data.