## 2 Related Work  
[PERSON] et al. [14] proposed the datAcron ontology to represent semantic trajectories at varying levels of spatiotemporal analysis and to demonstrate the procedure of data transformation via enhanced queries in SPARQL to support analytics tasks. Mobility analytics tasks are based on the volume and variety of data/information sources that need to be integrated. The proposed ontology, as a generic conceptual framework, tackles this challenging problem. The experimental results ([[http://www.datacron-project.eu/](http://www.datacron-project.eu/)]([http://www.datacron-project.eu/](http://www.datacron-project.eu/)), accessed 27 December 2022) in the domain of Air Traffic Management (ATM) demonstrate that the proposed ontology supports the representation of trajectories at multiple and interlinked levels of analysis. [PERSON] et al. [15] focused on extracting semantic trajectory patterns from geo-tagged data. They proposed a semantic trajectory pattern mining the framework from geo-tagged data taken from social media to create raw geographic trajectories. These raw trajectories are enriched with contextual/semantic annotations, using a ROI as stop to illustrate places of interest. The authors further enriched these places with multiple semantic annotations. Thus, each semantic trajectory is a sequence of ROIs with a set of multiple additional semantics. The algorithm returns basic and multi-dimensional semantic trajectory patterns. To successfully carry out a mission, high-precision tracking of a drone's trajectory is vital. Even if a quadrotor drone has great advantages, such as a lightweight structure, vertical takeoff, and landing capability, etc., it is also vulnerable to disturbance factors, such as aerodynamic effects, gyroscopic moments, wind gusts, etc. Thus, the implementation of an efficient controller for a quadrotor UAV is a challenging task. Towards this end, various approaches have been developed based on observer techniques [16], sliding mode control (SMC) [17; 18], neural networks (NN) [19], etc. However, these approaches are beyond the scope of this paper. Concerning trajectory analytics and visualization tools, [PERSON] proposed a general-purpose Python library for the analysis and visualization of trajectory data called Moving-Pandas [20]. The proposed library composes a combination of Pandas [21] and GeoPandas [22]. In MovingPandas, the trajectory is the core object, which is modeled as a time-ordered series of geometries, stored as GeoDataFrame, and integrated with coordinate reference system information. Depending on the domain and the purpose of the analysis, a trajectory object in MovingPandas can represent its data either as point-based or as line-based. Meanwhile, the analysis process and the visualization are executed in two-dimensional space. The proposed library can be used as a stand-alone Python script, as well as within the desktop GIS application QGIS as a plugin called Tajetools. [PERSON] et al. [23] proposed a software library called Yupi. The main goals of the proposed library were (a) to be abstract enough to be used across several domains and (b) to be simple enough for research with limited programming or computer vision knowledge. Yupi's modules contain special tools that allow for the creation of trajectories from video sources, artificial trajectory creation, visualization, and the statistical analysis of trajectories. To provide compatibility between the existing tools, the proposed software enables a two-way data convention among Yupi and third-party software through the Yupiwarp package. The authors validated their software and reproduced the results in selected research papers (with a few lines of code), demonstrating the effectiveness of the proposed software. [PERSON] et al. [24] proposed a Python library called scikit-mobility to provide a unified environment for the generation, analysis, visualization, and privacy risk assessment of human mobility data.