We present a generative approach to perform robot mapping that is based on an intelligent integration of static and dynamic entity classes within an environment, in order to extrap-olate map information at various resolutions. Our framework differentiates from the conventional standpoint where different mapping levels are overlaid on one another, by fusing information from different mapping levels that allows us to infer new information within partially mapped environments. Towards this goal, we develop a class-dependent map extrapolation function that captures the discriminative relation between an environment entity and the mapping procedure. We illustrate the advantages in using heterogeneous contextual information when mapping an environment using a prototype implementation of our approach on an indoor robot platform, giving very promising results.