## 1 Introduction  
Technological advances in the field of aerospace have led to the excessive use of UAVs (drones) in a wide range of domains. UAVs are able to operate manually, autonomously [1], or semi-autonomously [2], alone or in a swarm, providing a high degree of flexibility in the assigned mission. The market for UAVs has been steadily growing [3], from the delivery of goods/services and precision agriculture to surveillance and military operations. The effective modeling and analysis of UAVs' trajectories enable decision makers to acquire meaningful and enriched information/knowledge about the current situation in the field of operations, eventually supporting tool-based automated or semi-automated simulations for making predictions of high-level critical events. A semantic trajectory of a swarm of drones [4] is a synthesis of semantic trajectories [5] of multiple units moving (flying) in a specified formation, sharing common origin-destination points, having a common mission, enriched with semantic annotations at different levels of detail, and having one or more complementary segmentations, where each segmentation consists of a list of annotated episodes. A drone's trajectory is a sequence of points (traces) that specify the position of the moving entity in space and time. A segment is a part of the trajectory that contains a list of episodes. Each episode has a starting and ending timestamp, a segmentation criterion (annotation type), and an episode annotation. For example, an annotation type can be the \"weather conditions\", and an episode annotation can be \"a storm\", \"heavy rain\", \"extremely high waves\", etc. The utilization of drones in the cultural heritage and archaeology domain has increased and plays an increasingly important role [6; 7], since drones allow for an easy, quick, and low-cost solution for documentation by collecting aerial images and videos at different levels of altitude and angles. This is highly important, especially concerning the mapping, documentation, and detection of subsurface archeological sites [8; 9; 10; 11]. The effectiveness of UAV operations requires an efficient representation of their movement data, along with additional information about the context of the mission [5]. A semantic trajectory of a swarm of drones requires the recording of raw movement data, such as the latitude, longitude, and timestamp collected from each unit, along with external heterogenous data, such as mission details, points/regions of interest (POIs/ROIs), and weather data. However, unpredicted events (e.g., unit malfunction, weather conditions, security violations) and known operational restrictions of drones can cause incorrect, invalid, or even missing movement data. Thus, there is a real need for the reconstruction of trajectories using other data recorded during a mission. In the present work, the utilization of geo-tagged photos taken by drones' carrying documentation equipment during their flights (or obtained from other sources) is proposed, since these provide exploitable metadata for the process of trajectory reconstruction. For instance, in a potential scenario, a group of archeologists and geologists are interested in taking aerial photos of a petrified forest to document and obtain an initial mapping about the morphology of a geopark. Because the forest extends over a large and steep area, they decide to use a properly equipped drone to take photos of the landscape. The research team plan and upload the mission path in the drone's pilot software. Following the predefined path, the drone approaches the area of interest, taking photos during the planned mission (Figure 1). After landing, the research team analyzes the mission's photos and pilot data to acquire the trajectory followed by the drone, to validate the initial (planned) path. These data, along with data regarding weather conditions (ground temperature, humidity, etc.) from nearby weather stations, are necessary for the research team to obtain a general view of the area of interest.