A twofold architecture based on GNSS multipath environment prediction and detection is presented in a context of loosely coupled and tightly coupled IMU/GNSS integration for navigation in urban areas. A signal quality monitoring group of techniques is applied for a platform self‐contained effort to detect and exclude multipath‐contaminated GNSS signals. Additionally, the sensor integration Kalman filter stochastic model is adjusted on‐the‐fly based on a GNSS multipath environment map. The map is populated by crowdsourcing and contains the spatial distribution of average carrier‐to‐noise‐density ratio measurements, linked to the probability of non‐line‐of‐sight, multipath‐contaminated, diffracted, and attenuated signal reception. To address the map availability issue, a random forest machine learning model is developed to propagate the map to the city areas not directly surveyed by the mapping fleet based on open‐access geographic data. The architecture performance is evaluated in the automotive scenario showing 13‐17% accuracy improvement compared to a conventional Kalman filter.