In this paper we present an open and flexible approach for the standardisation of 3D geographical data, describing our physical environment in such a way that it can serve different applications. The aim of our approach is to keep the standard as simple as possible so that implementation in different software is straightforward and the reuse of once collected 3D data in different domains is optimally supported. Therefore, we propose to model the semantics of real-world objects independent from their application and we distinguish between the conceptual model and encoding. The result is a 3-layer approach, in which the first layer contains the conceptual model: the object types with their definitions and properties. This layer reuses definitions of various existing models (national and international) as much as possible. The second layer contains the modelling constraints: the set of rules that define how the objects from the conceptual model are represented in 3D as needed for a specific context or application. This second layer contains additional (3D) requirements to standardise the 3D representations of the objects. The third layer contains encoding profiles, thus specifying how different formats can best be encoded; these formats could be JSON or XML/GML.

In this paper we motivate and describe our approach. For a small area we have developed a prototype that implements the 3 different layers. The prototype shows how the approach can be implemented for one specific application and additionally it provides insight into further development.

Geospatial data is an increasingly important information asset for decisionmaking, from simple every day decisions like where to park your car, to national and international policy on topics like infrastructure and environment. Because of the location aspect, geospatial data is often the linking pin between different datasets and therefore important for data integration. A lot of geospatial data is created, for example, as part of governmental processes and nowadays, also disseminated as open data, traditionally through "Spatial data infrastructures" (SDIs). There is a lot of potential for reusing this data in other domains than the domain and use case for which it was originally created. My main research question was: "How to reuse geospatial data, from different, heterogeneous sources, via the web across communities?" Several aspects of data dissemination must be addressed before open data is actually in a good position for getting reused. These aspects have been coined the "FAIR principles": findability, accessibility, interoperability, and reusability.