# Geo-Spatial Context Provision for Digital Twin Generation
## 4 Related Work
### Geo-Spatial Context, LiDAR Technology, and Digital Twins
The life-cycle perspective supported by keeping the twin up-to-date and remembering previous data is also a crucial aspect of our proposed digital twin. Approaches like [13] also aim to structure the information that is part of a digital twin in a form that facilitates the work of the primary stakeholders (in this case the power line operators). However, for many of the scenarios listed above, the behavior of the twinned object is not necessarily relevant for the tasks that its users should be supported in. Therefore, the focus lies on the geo-spatial information as anchor-point for integrating other data. For scenarios like the presented illustration case, where system behavior over time plays a crucial role, our digital twin architecture can provide benefit to its stakeholders by allowing access to information structured along a relevant business process. Here, the used subject-oriented notation can depict behavior of heterogeneous actors in a unifying way and with a minimal number of notational elements, facilitating the intelligibility of digital models for process stakeholders. A selection of existing research also concerns the digital twinning of the LiDAR technology itself, either as a singular entity or as part of a larger system. Such a digital twin was, e.g., discussed in [41], with the authors aiming to build a reduced complexity model of a Flash-LiDAR system as basis. The intended DT usage concerns performing simulations and producing point cloud data, which can be used as basis for the training of neural networks that can subsequently perform detection and correction of aberrations in the LiDAR system. In [42], the authors used a digital twin to evaluate the potential of LiDAR mounted on an unmanned aerial system to detect various air collision risks, such as objects that are small, uncooperative, and unpredictable. A twin of the port of Hamburg was enriched with the necessary agents (air collision hazards, unmanned aerial system, mounted LiDAR simulation) to simulate and test different scenarios. Similarly, the authors of [43] created a digital twin to simulate and test a virtual service robot aimed at providing care to the elderly or people with development disabilities within its environment. The functionalities of the socially assistive robot that were tested included robot navigation, fall detection of a patient, and gesture recognition. The authors stated that three twins were created, one of the room, the service robot, and an elderly virtual avatar, respectively. As part of the robot twin, a virtual LiDAR sensor that generates data was simulated. In [44], a digital twin of a CPS was created. The twinned system encompasses two industrial robots automatically conducting measurements for LiDAR sensors aimed to be used in the aerospace sector. For this purpose, one robot holds a LiDAR sensor and the other holds a target material. Digital twins of the different components were created, including the LiDAR sensor. Different twins were used in a variety of different ways, acting as a virtual test object (to plan trajectories), virtual mirror (to observe the current state), mediator (to actuate the real object), and mental model. As part of our approach, the LiDAR sensor itself is insofar part of the digital twin, as that it can be either modeled as its own subject with the corresponding behavior or included within another subject representing the behavior of a larger CPS component (e.g., the robot system as a whole). The data provided by the sensor is given to the digital twin and documented alongside (current or past) instances of behavior. The focus therefore lies on run-time monitoring of the system. In comparison, most of the existing approaches outlined above focus on the simulation of a system to test different functionality. In [42; 43], the LiDAR sensor is considered because the data that it provides is crucial for what should be simulated, tested, and evaluated. In [41], the twin aims to support data collection for the purpose of training neural networks for LiDAR recalibration.