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With the increasing availability of Cooperative Intelligent Transport Systems, the Local Dynamic Map (LDM) is becoming a key technology for integrating static, temporary, and dynamic information in a geographical context. However, existing ideas do not leverage the full potential of the LDM approach, as an LDM contains streaming data and varying implicit information which are not captured by current models. We aim to provide a semantically enriched LDM that applies Semantic Web technologies, in particular ontologies, in combination with spatial stream databases. This allows us to define an enhanced world model, to derive model properties, to infer new information, and to offer expressive query capabilities over streams. We introduce our envisioned architecture which includes an LDM ontology, an integration and annotation framework, and a stream query answering component. We also sketch three …