## 1 Introduction
The team realizes that the pilot data for a specific part of the flight are missing due to a unit malfunction. Thus, it is not possible to reconstruct the whole trajectory of the drone to cross-validate flight details, nor to create its semantic trajectory to acquire a more comprehensive view about the area of interest and its context. Figure 1: Drone’s flight area of interest in the presented scenario, taking photos (blue pins) during a cultural heritage documentation mission (green and red pins represent the start and the end of the flight). The evolution of systems for the real-time processing of UAV data for wireless networking and other related technologies, such as GPS and high-resolution cameras, allows for the constant and remote sharing of the movement of swarms of UAVs. These raw spatiotemporal data are the basis for the construction of UAVs' trajectories. However, analyses of these trajectories often produce poor and restricted results. To achieve a more efficient analysis of trajectories, contextual information e.g., on the environment, weather, and mission details, are required [12]. Most times, such data are heterogeneous, gathered from disparate sources, making their semantic enrichment and integration a difficult task. Ontologies have been adopted in various domains, offering a powerful tool for the semantic representation and integration of data, reducing the structural and semantic heterogeneity among the various data sources, specifying semantics in a clear, explicit, and formal way [13]. The aim of this paper is to present our latest efforts (a) to implement an ontology-based, tool-supported framework for the reconstruction of drones' trajectories using geo-tagged photos; (b) to enrich the reconstructed trajectories with external data; (c) to semantically annotate the enriched trajectories using an ontological approach; and (d) to utilize semantic queries in SPARQL to support simple analytics tasks. The goal is to implement the above modules as extensions on the well-known and widely used MovingPandas platform, delivering a novel framework, namely, the ReconTraj4 Drones framework. We have evaluated the implemented solution using real datasets (photosets) collected during cultural heritage documentation missions, as well as open datasets. ReconTraj4 Drones is currently the only free and open-source trajectory management tool that supports (a) trajectory reconstruction using geo-tagged photos and (b) the annotation and analytics of the reconstructed and semantically enriched trajectories. By reviewing related works, it is concluded that these emphasize the trajectory reconstruction process based on (i) photos found in sources such as Flickr, (ii) videos, and (iii) users' posts on social media. These approaches do not provide a freely available and open-source tool for testing and evaluation. In this paper, we compare ReconTraj4 Drones with two related tools, and we evaluate its performance against several criteria using four different datasets. This paper is an extension of our recently published work [4]. This extension (more than 50%) mainly concerns (a) the implementation of the proposed framework, namely, ReconTraj4 Drones, as an extension of the MovingPandas platform and (b) an evaluation of the implemented framework by conducting experiments with real datasets (inhouse and open ones). The structure of the paper is as follows. Section 2 presents related work regarding semantic trajectories, trajectory reconstruction from geo-tagged photos, and trajectory analytics and visualization tools. Section 3 presents the implemented framework that extends the functionality of MovingPandas. Section 4 presents the datasets used for the evaluation of our solution and the produced results. Section 5 critically discusses related work and the developed solutions, comparing these based on specific requirements, the present experimental results, and future work. Finally, Section 6 concludes the paper.