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Remotely sensed imagery provides invaluable information for rescue and relief efforts, damage assessment, and the planning of remedial measures (Hussain et al., 2011). With UAS, disaster zones can be observed and assisted promptly. Aljehani and Inoue (2019) designed a system that incorporates UAS scanning and UAS tracking to generate a safe map with potential routes for refugee evacuation. They have successfully utilized UAS to detect and track pedestrians by the Haar‐cascade and Kalman filter classification method, which can serve as a substitute for participatory tracking (e.g. mobile tracking) when pedestrians are not carrying mobile devices. Practically, they proposed using mobile networks for communication when the standard communication channels are completely lost. They also stressed the importance of human operators in the evaluation process and mission management (Aljehani and Inoue, 2019).

After a disaster, accurate and swift searching for the affected population and understanding their needs (e.g. water, food, medical assistance, etc.) is urgent and challenging. With the aid of UAS, humanitarian relief can be more efficiently delivered. Bravo et al. (2019) developed a Partially Observable Markov Decision Process (POMDP) to improve UAS path planning by giving higher priorities to the areas that are more likely to have victims. They showed that the method can provide the full coverage of the affected area and optimize the time to find the victims groups in three different cases, namely a tornado in Xanxerê, Brazil, a refugee camp in South Sudan, and a nuclear accident in Fukushima, Japan, which only took no more than 10 minutes, 5 minutes, and 2 hours, respectively (Bravo et al., 2019). However, they also mentioned that the use of UAS can be limited by the location, sight, limited base distance, battery life, and the concerns of social acceptance and airspace access.