In recent years, a lot of work was conducted in order to give robots the ability to gather semantic information about their environment, store it, represent it for the user and to perform high-level tasks based on the semantic information. Most of the systems use internal representations of the gathered information that is not intuitively understandable by humans and that is inadequate for learning from commonly available sources. The combination of object/place classification and common-sense knowledge to semantic maps found its way into indoor semantic mapping approaches to improve human-robot interaction. The aim is to assign complex task settings to the robot allowing it to guide the search for the solution by itself. In this paper, we present a formal common definition of semantic maps. We discuss different criteria for designing and classifying semantic maps and their appropriate challenges. Furthermore, we present an outdoor semantic mapping approach incorporating common-sense knowledge into the classification process and a suitable map representation for high-level tasks.