## 2 Related Work
The authors of [21] present a drone that utilizes multi-sensors and geolocalization to detect survivors after natural disasters as part of search-and- rescue operations. The autonomous and robust drone is also equipped with an ultra-wideband radar, microphone array, camera, RFID reader, laser range finder and LIDAR at an altitude of 13 m. The results suggest the capability of early-stage survivor detection; however, the authors promise further development of the prototype in relation to the camera module and propagation model. The authors of [22] used a drone as the first responder in natural and/or man-made disasters due to its rapid response, as well as its adaptability to particular environments. The first-responder drones (FRDs) system implemented a long-range (LoRa) system, which is enabled by low power-wide area network (LP-WAN), as well as a GPS module and a camera onboard. The results suggest that the proposed FRDs could respond within a range of 10-46 s of a footprint up to 30 km in rural areas; the range decreases as the drone moves towards urban areas. Moreover, increase the distance between a drone and the terrestrial receivers is an issue that is repeatedly highlighted in the literature. Where it is argued that optimizing propagation path loss and power consumption would be key parameters to enhance wireless connectivity at different drone altitudes. In [23], a drone model is shown that uses a deep-learning algorithm to enhance aerial image processing for search-and-rescue operations that deal with human lives. At an altitude of 61 m, with a small onboard GPS device and a colored camera, the proposed drone taken thousands of human pictures, before using them as training, test, and validation primary data for the deep-learning algorithm. The results show a high level of sensitivity with an acceptable false-positive rate. For further work, thermal cameras at different drones altitudes could be used to improve the search-and-rescue operations further. The authors of [24] present an architecture of human detection during or after natural disasters using UAVs integrated with head-mounted display (HMD) devices. This architecture aims to deliver a solution to protect human lives in search-and rescue operations. Support vector machines (SVM) and adaptive boosting (AdaBoost) are traditionally used for human detection via an RGB camera installed on an UAV. The experimental results show reasonable object detection architecture with search-and-rescue operations via RGB images. Thermal cameras are recommended to improve detection in low light and/or in camouflaging foliage scenarios. In [25], a multipurpose UAV is presented to carry out a rescue operations due to avalanche events in mountains. The aerial platform is integrated with avionics that meet the environmental requirements for mountainous terrain (e.g., low temperatures, high altitude, and strong winds). The model uses a visible thermal camera for search-and-rescue missions involving missing persons in snow and in woods during any time of the day. Results show that the proposed model can operate autonomously and in quick search operations at different altitude. The thermal FLIR Tau 2 IR camera shows an acceptable level of performance for snow-covered bodies up to 20 cm. There is a trade-off between flight endurance and payload weight against power consumption. The detection of the heat profile to locate missing persons using drones integrated with a thermal camera is considered in [26]. The proposal calculated target coordinates of a missing person using a closed-form formula. The results indicate that UAV can detect humans lying or sitting on the ground as well as identifying their location. The authors of [27] introduced an overview of drones used for hyperspectral remote sensing at low altitudes. The drone payload's weight, size, flight duration and cost are criteria that help to choose the right payloads (e.g., sensors) and drone design.