## 3 Materials and Methods
### Trajectory Visualization  
Visualization is of high importance in the management of trajectories. Even though MovingPandas provides a functionality for trajectory visualization, ReconTraj4 Drones implements an additional, self-contained module for this purpose. The aim is to visualize the reconstructed trajectory with all the additional information that is used for its enrichment and its segmentation. As already stated, the additional information concerns data beyond latitude, longitude, and timestamp that uniquely define each point of the trajectory. Such data concern the drone's mission and flight details, the type of drone, the flight altitude, the camera model and maker, and finally, the geo-tagged image itself that is related to a particular point. Moreover, if the enrichment of a trajectory has been carried out using weather and/or POI data, then this data can also be visualized. Subsequently, the aim is to visualize not only the raw trajectory, but also all the external integrated information that is obtained for each point of this trajectory.  
To implement the abovementioned requirement, ReconTraj4 Drones uses the Folium library ([[https://python-visualization.github.io/folium/](https://python-visualization.github.io/folium/)]([https://python-visualization.github.io/folium/](https://python-visualization.github.io/folium/)), accessed on 30 December 2022), an open-source and free library that utilizes the OpenStreetMap to visualize trajectories. This library also enables the creation of interactive visualization maps. Interactive in this context means that not only is each point of the trajectory displayed with a special symbol (pin), but also that these symbols are clickable, hiding all the information available for that point. In addition, the starting and ending point of the trajectory are assigned with special symbols of different colors, i.e., green for the starting point and red for the ending point.  
Having said that, because the reconstruction of the trajectory is based on the number of geo-tagged photos and the time interval between them, it is observed that, in cases of short time intervals between geo-tagged photos, the number of points is very large. Consequently, visually capturing all these points makes the trajectory unreadable (Figure 6a). To solve this problem, a percentage of the total amount of points is visualized only. This percentage is defined by the user (Figure 6b).  
Last, but not least, in ReconTraj4 Drones, it is possible to visualize the different trajectory segments, as well as the detected stops, if the trajectory segmentation and/or trajectory stop detector modules have already been executed. In the first case, each trajectory segment is visualized with a unique color to distinguish between them. In addition, the starting and the ending point of each segment are indicated with green and red color pins, respectively. On the other hand, if the segmentation process has not been executed, then the entire trajectory is defined as one segment and is visualized with the same color. Regarding stops' visualization, each trajectory stop is visualized as a red circle. Each circle is a clickable item displaying all the information about that stop. The user determines whether segments and/or stops will be displayed, even if both modules have already been executed.