## 6 Conclusions  
The market for UAV applications has been steadily growing, pointing to UAVs' great potential in a variety of domains, such as the military, precision agriculture, documentation, surveillance, and the delivery of goods/products/services. The management and analysis of drones' movement data are becoming more and more popular, especially in the era of emerging technologies (IoT, robotics, AI, and big data analytics). Moreover, to derive meaningful information from these data, their semantic annotation and enrichment with external heterogenous data is required. Factors such as unexpected UAV malfunction, extreme weather conditions, or hostile actions (e.g., hacking) can cause incorrect, invalid, or even lost movement/trajectory data, making the trajectory reconstruction from other data (e.g., images, video) a demanding task. To respond to this problem, we have implemented a novel framework that reconstructs semantic trajectories of drones from geo-tagged photos collected during their flights. Moreover, the proposed framework integrates tasks that support the enrichment, segmentation, visualization, semantic annotation, and analysis of the reconstructed (semantic) trajectories. Such functionality has been implemented as an extension to the well-known and widely used MovingPandas platform, as a free and open-source analytic and visualization system of drone's semantic trajectories called ReconTraj4 Drones. The evaluation of the implemented system with real datasets demonstrates that the reconstruction and modeling of drones' semantic trajectories using geo-tagged images can be efficiently supported by the proposed framework.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{\begin{tabular}{c} **Dataset** \\ **Size** \\ **(Image** \\ **Files**) \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Trajectory** \\ **Length** \\ **(m)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **a** \\ **(MB)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **b** \\ **(MB)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **c** \\ **(s)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **d** \\ **(s)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **e** \\ **(\%)** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **f** \\ **(\%)** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **g** \\ **(Triples)** \\ \end{tabular} } \\ \hline Petrified Forest & 206 & 1297.4 & 417 & 360 & 117 & 103 & 14.6 & 2.4 & 10,462 \\ \hline Vrisa Village & 431 & 5366.7 & 402 & 388 & 67 & 235 & 8.6 & 1.4 & 21,262 \\ \hline University Hill (Phantom 4 Pro) & 15 & 193.3 & 368 & 363 & 4 & 8.5 & 7.5 & 3.8 & 1294 \\ \hline University Hill (Inspire 2) & 26 & 193 & 373 & 364 & 7.5 & 14.6 & 5.2 & 8.8 & 1822 \\ \hline University of Konya & 439 & 2804.2 & 380 & 367 & 119 & 240 & 13.4 & 2.9 & 22,646 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental results of ReconTraj4 Drones evaluation.  
**Author Contributions:** Conceptualization, K.K.; methodology, K.K.; software, A.S.; validation, A.S., K.K.; formal analysis, A.S., K.K.; investigation, A.S., K.K.; resources, A.S., K.K.; data curation, A.S., K.K.; writing--original draft preparation, A.S.; writing--review and editing, K.K.; visualization, A.S.; supervision, K.K.; project administration, K.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
The authors acknowledge the contribution to this work made by members of the research team of the \"Research e-Infrastructure [e-Aegean R&D Network], Action 1.2 e-Aegean Geospatial data services\" project, code number MIS 5046494, implemented within the framework of the \"Regional Excellence\" Action of the Operational Program \"Competitiveness, Entrepreneurship and Innovation\".  
The authors declare no conflict of interest.