# Geo-Spatial Context Provision for Digital Twin Generation
## 1 Introduction
In this article, we explore the structuring of geo-spatial information, as derived from 3D point cloud data, in order to consolidate it with relevant information required for designing and running digital twins as digital threads (cf. [17]). Hence, we presume that the raw 3D scan data has already been processed to be further utilized as geo-spatial context information. Focusing on geo-spatial context integration requires an appropriate representation and visualization of this information [18; 19; 20]. In cyber-physical settings, scans conducted with LiDAR technology are used to capture the operational environment of CPS, and to enable a CPS to accomplish its tasks as part of a business case or process [21]. We showcase how a wider view of the concept of digital twins, where not only data about the physical object, but also its behavior is captured, can facilitate the structuring of scan data produced alongside the operation of the system in a way that it becomes better accessible to relevant stakeholders. Such a conception of digital twins has recently emerged in CPS research (see [8]) and has potential for structuring a variety of data produced during operation. Process modeling (which is the basis for such a kind of behavioral twins) was also identified as a currently emerging research direction in a recent bibliometric analysis of the field of digital twins [22]. We contribute to this new research trend by providing a digital twin architecture based on subject-oriented process models [23]. We exemplify a logistics showcase of a digital process twin architecture and demonstrate its models' potential for context-sensitive geo-spatial applications. The behavioral specification of the CPS is created utilizing subject-oriented process models. These (executable) models are used as a basis to structure scan data alongside the conducted behavior of the CPS (components). Compared to simply structuring scan data in the form of a time series, using process models has the advantage of associating produced information directly with the behavior of a system at a certain point in time. This additional context enriches the digital twin with a behavior perspective for each system component and the components' interplay representing the overall system behavior. Once this information can be conveyed to relevant stakeholders, the environmental context at different points in time can facilitate the integration of cyber-physical systems into business operation. This context can be particularly considered for supporting decision makers and process monitors. The article is structured as follows: Section 2 introduces the vaccine logistics use case, giving insight into the general workflow, and how and when LiDAR technology can be applied to obtain geo-spatial context information. Based on this scenario, Section 3 provides the proposed digital twin architecture and exemplifies models of LiDAR-relevant parts of the logistics process. In particular, the integration of behavior models with scan data is explored. Along with the architecture, a methodology is introduced to guide implementing the proposed architecture. An application for behavior monitoring is also outlined. Section 4 highlights related work. First, the use of LiDAR technology and the produced scan data in digital twin research is examined. The proposed architecture is positioned in relation to existing approaches and its difference and potential are detailed. Secondly, methodologies for the creation of digital twins are explored to reflect on the presented methodological guidance and to identify topics of future research with respect to development procedures. Section 5 discusses in how far the knowledge base on subject-oriented design and engineering could be enriched. Finally, Section 6 briefly summarizes the presented work and discusses the results. Limitations and possibilities for future investigations are outlined.