OpenStreetMap data comprise of very detailed (e.g. zebra crossing) and quite rough features (e.g. built-up area). But making large scale maps from data with inconsistent level of detail often blurs map comprehension. This paper explores the automatic harmonization of OpenStreetMap data for large scale maps, i.e. the process that transforms rough objects to make them consistent with detailed objects. A typology of the new operators that harmonization requires is presented and six algorithms that implement the operators are described. Experiments with these algorithms raise several research questions about automation, parametrization, or the level of abstraction of the transformation, which are discussed in the paper.