# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 2 Background and Related Work
### UAVs and Data Fusion Techniques  
UAVs can be defined as small aircraft that can be operated remotely by pilots or programmed to operate without the assistance of humans. UAVs were originally developed for military purposes, but they are now a major focus of research in several disciplines. The technical characteristics of UAVs play a significant role in their categorization, such as technology used, level of autonomy, size, weight, and energy resources. UAVs are often equipped with a variety of sensors, including radars, television cameras, global positioning systems (GPS), satellite communications, image intensifiers, and infrared imaging technology [27]. Nowadays, UAVs are used in many application domains, such as: precision agriculture [28], construction and infrastructure inspection [29; 30], and rescue of people [31] and also in the same context of this article--disaster management. For example, a literature review on the application of drones in the disaster management context is presented in [26]. The authors of that work have analyzed papers from 2009 to 2020 and classified them into four categories: (1) mapping or disaster management; (2) searching and rescuing; (3) transportation; and (4) training. UAVs can be also prepared for specific missions such as face recognition. In [32], for example, a deep neural network model is presented to improve the performance of face detection and recognition tasks when photos are taken from high altitudes. However, in the case of earthquakes, detecting living persons under the rubble is an important concern, where no face information is available. Our article does not focus on the algorithms which can be adopted in the implementation of data sources. Nevertheless, such techniques can be adopted to improve the quality of data sources suitable for certain disaster types. There are attempts to measure the concentration of gases and aerosols in the atmosphere by installing the necessary equipment on board commercial airliners [33]. The samples are collected with an inlet probe, which is connected to the equipment on board. This allows accurate sampling of aerosol and chemical contents. The samples are analyzed in ground-based laboratories. These data can be used, for example, in cases where forest fires have to be monitored. Such techniques can be linked to other data sources such as satellite data and to UASs ascending in situ. It is stated that although there is a significant increase in the number of publications on use of drones in disaster cases, a limited amount of research is observed to address post-disaster healthcare situations especially with regards to disaster victim identification. Another topic related to this paper is remote sensing [34], which is used to describe information that is gathered from distant targets. This is usually done by satellites, aircraft, and UASs. An interesting example of remote sensing using imagery taken from commercial flights is presented by [PERSON] et al. [35]. Data fusion techniques are introduced to enhance the accuracy and reliability of multiple but related data sources [10; 36; 37]. In [37], for example, a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs) is presented. The analysis performed by the authors revealed that a combination of data fusion and SLAM can assist in autonomous UAV navigation without having a predefined map. In this approach, raw sensor data are directly provided as an input to the SLAM algorithms. The data fusion process may be made more end-to-end using machine learning techniques. These techniques are suitable in enhancing the effectiveness and efficiency of data detection by UAVs, for example, to improve situation awareness. Another related work is presented in [38], in which a survey on UAV orthoimage generation technologies that focus on mainstream frameworks is presented.