The paper describes a map generation system which relies on random individual contributions of GNSS (Global Navigation Satellite Systems) traced movements for outdoor scenarios and traces from a RF-based indoor localization system. In a typical use case, mobile phone users would join a specific community to contribute their movements along streets, roads or pathways in form of so called journeys. The proposed algorithms presented in his paper are also able to generate a precise map for indoor scenarios, with which certain changes in the accuracy of the RF localization system can be mitigated. Neither contributing subscribers nor the map generation need to have an a priori knowledge of the charted area. The approach presented here comprises the journey recording, the upload process to a common server and the processing algorithm for map generation. The filter mechanisms and adaptive plausibility checks applied to the raw data are key components for the deduction of precise street and movement maps. Furthermore, the proof of concept is given for different in- and outdoor application scenarios to demonstrate the adaptivity of the algorithmic approach.