# Geo-Spatial Context Provision for Digital Twin Generation
## 1 Introduction  
A core component of Cyber-Physical Systems (CPS) are sensors to obtain information about the physical world [1]. They enable capturing highly relevant environment data, including geo-spatial context information, to successfully operate an embodied CPS. Light detection and ranging (LiDAR) technology has been used to create detailed scans of physical objects, which can become the basis for digital representations of these objects. LiDAR technology is, e.g., used to capture real-time data concerning the surroundings of autonomous vehicles to detect and recognize objects [2]. It is also applied in the context of indoor navigation of autonomous systems for the complex task of simultaneous localization and mapping (SLAM) [3]. Scan data has also become a crucial building block for the creation of digital twins [4]. Digital twins (DTs) are traditionally seen as simulated representations of a physical object or a set of objects (cf. one of the earliest definitions of the term provided by the NASA in [5]). Currently, digital twins are often used in the context of industry to represent objects that are part of production (production systems, material, products,...). Especially in the context of Industry 4.0 and smart manufacturing, digital twins are seen as a promising technology--cf. [6] with application areas pertaining to product design, production, and prognostics and health management. Digital twins can serve as a design and engineering prototype for physical objects and allow for testing of different functionality [7]. In the context of CPS, executable behavior specifications in the form of subject-oriented processmodels were proposed for exploring and validating CPS behavior in the form of a digital twin thread [8; 9]. Digital twins aim to serve as a single hub of information about the real-world object(s) for a user, linking systems and realizing the structuring, monitoring, and exploitation of data [10]. Therefore, structuring the data produced by a CPS during its operation is a crucial aspect of digital twins to enable the monitoring of the system and its environment while it executes its behavior in the respective CPS context, e.g., business processes. To this end, the information needs to be structured in a form that makes it accessible to the relevant stakeholders, leading to usage-driven deployment of digital twins and thus user acceptance [11]. Although geo-spatial information is considered to provide useful context data, one challenge concerns deriving relevant information from the created raw scan data (i.e., 3D point clouds). In this context, semantic segmentation, i.e., classifying every point in a scene and assigning it a label to give the data semantic meaning, has been tackled extensively in the literature through a variety of approaches, recently propelled forward by deep learning (see [12]). Performing such segmentation tasks is often a part of the early stages in the digital twinning process (see [13; 14]). Different formats for 3D models have furthermore been proposed in a variety of different contexts to structure the relevant produced information (e.g., data formats for digital city models (see, e.g., the City Geography Markup Language (CityGML) at [[https://www.ogc.org/standards/citygml](https://www.ogc.org/standards/citygml)]([https://www.ogc.org/standards/citygml](https://www.ogc.org/standards/citygml)), retrieved 8 June 2022) that highlight buildings, vegetation,...). A further challenge concerns the (real-time) visualization of created digital representations (cf. [15]). Depending on the intended DT use, the produced geo-spatial context needs to be potentially integrated with other information to serve a specific purpose (see the DT created in [16], which incorporates data from different auxiliary sources). In the context of CPS, the produced geo-spatial data needs to be linked with other information, e.g., be embedded with other sensor data, since one of the core added value propositions of DTs lies in making use of linked information [10].