## 4 Discussion  
Search and rescue missions usually require an updated situation of the search area as quickly as possible. However, in addition to the time needed for generation of updated maps (which depends on the number of images to be processed, the spatial resolution and the extent of the captured area) [61; 62], time needed for acquiring images on the processing site can also postpone the whole process completion for hours. Therefore, transmitting images acquired by a UAV camera during flight is an essential demand for SAR mission success. The measured throughput confirmed that the proposed system with the chosen data link module can be used for real-time transmission of high-resolution images acquired with a camera on a UAV. This is in line with the declared data rate of 20 Mbps at 5 km. The measured throughput at a 5-km distance was ultimately lower (1.4 MB/s or 11.2 Mbps), but that result was expected. During the tests, the connection stability was dependent on the presence of obstacles between the transmitting and receiving ends. Recent research on long-range communication for unmanned search and rescue aircraft systems [63] reported a single-link throughput of 10.6 Mbps using a local dedicated Long-Term Evolution (LTE)  
Figure 14: Test 5—The best throughput results for various distances and UAV antenna yaw angles. Figure 13: Test 5—Throughput for various distances and UAV flight altitudes (0\({}^{\circ}\) yaw angle). network and a quite stable multi-link throughput of \(\sim\)20 Mbps, but our approach is not dependent on the third-party infrastructure, while still providing sufficient throughput. In the paper [64], the authors developed a rapid aerial monitoring system for emergency responses which can successfully acquire the sensory data of a test site in real time. By processing the data, the system can produce high-quality orthoimages. The GSD of the presented experiment was 3 cm and single-image ground coverage was 145 \(\times\) 90 m. However, there is no information on the achieved (and measured) throughput of images to the ground station. Although our achieved throughput results are lower than reported in [16], that approach uses a high image compression (0.4% of the original size) before transmission. Therefore, additional use of the acquired images for searching for a lost person using some image processing algorithm [15; 65] is unlikely, and detection results would be poor because of lost details in the received images. Our demands for a high resolution, a high data rate and a low system price are the limitations for satellite-based image transmission [66]. Our next challenge was the optimization of the subsequent step in the planning process, which was creating a mosaic of transmitted images and georeferencing it on a map using GIS software. For that purpose, we developed a QGIS plugin for search and rescue mission planning. Currently, within the plugin, two methods for stitching images are available (top left corner in application GUI, presented in Figure 3). The first method uses Image Composite Editor (ICE), an advanced panoramic image stitcher created by the Microsoft Research Computational Photography Group [67]. This free development tool does not provide data needed for georeferencing output results into a map. Therefore, an additional algorithm was developed for the calculation of the correct GPS position and orientation of the resulting map part. It uses the GPS data from each single image used for generation of the map part. Another method is using a commercial solution for mosaicking images. For this purpose, the Agisoft Metashape software [29] was used. A request for image stitching is generated within QGIS software as a Python script and the result is automatically imported into the QGIS environment.