## 5 Discussion  
To the best of our knowledge, ReconTraj4 Drones is currently the only free and open-source trajectory management tool that implements trajectory reconstruction using geo-tagged photos, semantic annotation, and analytics in reconstructed and enriched semantic trajectories. In Table 1, an evaluation of related works based on specific criteria is presented. More specifically, the evaluation concerns the capability of related works with respect to trajectory reconstruction using other data sources beyond GPS data, the support of tasks such as segmentation and stop detection, the enrichment of trajectories with external data, their interactive visualization, and finally, the semantic annotation and analytics of the reconstructed semantic trajectories. The columns correspond to the functionalities/tasks, and the rows correspond to the related studies. By reviewing the related works concerning the reconstruction of trajectories, it can be concluded that they emphasize the trajectory reconstruction process based on (i) geo-tagged photos in sources such as Flickr, (ii) videos, and (iii) users' posts on social media. However, these approaches do not provide a freely available tool for testing and evaluation. Our aim was to compare the ReconTraj4 Drones framework with other implemented frameworks related to trajectory reconstruction using geo-tagged images and to measure its efficiency in terms of time, space complexity, and scalability; however, this was not possible due to the aforementioned reason. We have evaluated the performance of ReconTraj4 Drones using the datasets described in Section 3.1. The experiments were conducted using a laptop computer with an Intel Core(TM) i7-2670 QM 2.2 GHz CPU with 8 GB RAM and a 50 Mbps internet connection. The framework's evaluation (Table 2) was based on the following criteria:  
* Memory used for the reconstruction of raw trajectory (MB). * Memory used for the enrichment process (MB). * Execution time for the reconstruction of raw trajectory (seconds). * Execution time for the enrichment process (seconds). * CPU usage for the reconstruction of raw trajectory (percentage). * CPU usage for the enrichment of raw trajectory (percentage). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Tools**} & \multicolumn{3}{c}{**Reconstruction**} & \multirow{2}{*}{**Segmentation**} & \multirow{2}{*}{**Enrichment**} & \multirow{2}{*}{**Semantic**} & \multirow{2}{*}{**Interactive**} \\  & \begin{tabular}{c} **Geo-** \\ **Tagged** \\ **Images** \\ \end{tabular} & & \begin{tabular}{c} **Social** \\ **Media** \\ **Posts** \\ \end{tabular} & \begin{tabular}{c} **Step** \\ **Detection** \\ \end{tabular} & **Segmentation** & **Enrichment** & \
\begin{tabular}{c} **Semantic** \\ **Annotation/** \\ **Analytics** \\ \end{tabular} &
\begin{tabular}{c} **Interactive** \\ **Visualization** \\ \end{tabular} \\ \hline Yupi & - & \(\surd\) & - & - & - & - & - & - \\ \hline scikit-mobility & - & - & \(\surd\) & \(\surd\) & \(\surd\) & - & - & \(\surd\) \\ \hline ReconTraj4 Drones & \(\surd\) & - & - & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of available tools for trajectory reconstruction. Figure 16: An example SPARQL query defined by user in Jupyter notebook environment. Despite the successful reconstruction, enrichment, and semantic annotation of drones' trajectories using ReconTraj4 Drones, some limitations and restrictions were identified. A restriction concerns the integration of historic weather data. Particularly, the accuracy of the weather data is at the level of hours (not in minutes). As a result, all the points recorded during the same hour correspond to the same weather data. Regarding the Meteostat Web service, the returned weather data based on altitude were null in most of the cases. Thus, the weather data of the ground were mostly used.