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High definition (HD) maps can fail by becoming outdated. To still use them safely for automated driving, they need to be verified or updated, both requiring methods for change detection. We propose two significant improvements for HD map change detection that do not require a highly accurate localization prior as localization quickly fails or cannot be trusted in an outdated map. Given a very coarse localization prior, we group stored or measured map features in spatially and semantically separable feature groups. These feature groups are not only intuitive, like the sequence of leftmost dashed lane markings, but changes are also highly correlated within them. The first contribution improves the way internal consistency of each feature group is assured by using boosted classification trees. Additionally, a mutual evaluation scheme is added for all seemingly unchanged feature groups. Always one feature group is …