# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 2 Prerequisites for Using the UAVs in Some Spheres of Economy
### Exploration of Minerals (EoM)
Other potential applications include rare earth element mapping and mine tailings monitoring [62]. When searching for deposits, it is necessary to analyze a large amount of data: space images, soil and groundwater samples, presence of minerals-indicators, geophysical survey data, and many others. It is difficult for a person to process such a large amount of information. In particular, at the stage of exploration, identification of potentially prospective sites of ore, oil, and gas can occur after determining the correlation of field and well survey data; laboratory analysis of samples; and data recorded by drone sensors for ore, oil, and gas bearing and low potential sited. This problem has many parameters and hidden dependencies; it cannot be formalized and accurately solved by classical methods, but approximate solution can be obtained by machine learning methods. Algorithms such as artificial neural networks (ANN), support vector machines (SVM), and convolutional neural networks (CNN) are most often used in exploration geophysics [63]. The set of sensors installed on the UAV is determined by the task, but most often it is a hyperspectral camera, magnetometer, and GPS [60]. Due to the limitations imposed on the weight and size of the equipment, a camera operating in the VNIR band (400-1100 nm), such as RicOLA, is usually installed as a hyperspectral camera [64]. The spectral characteristics in this range allow the detection of iron oxides (range 800-1000 nm) as well as rare earth elements (Nb, Ta,... ) [65]. Data validation is performed by ground-based measurements, in particular, X-ray fluorescence analysis, X-ray diffraction analysis,  
Figure 5: The use of UAV at various stages of geological exploration and mining. Source: Generated by the authors. and optical microscopy [47]. Apart from VNIR range cameras, a SWIR range camera (1400-3000 nm), e.g., SWIR 640 P-Series, can be installed if necessary [66]. In general, it is necessary to work in a variety of spectral ranges to find different groups of minerals [67]. Given the presence of large uranium deposits in Kazakhstan, it is also necessary to equip the drone with a radiometer. The main problem of remote sensing with drones is the need to maintain accurately the height above the ground, bypassing the terrain elements. If this condition is not met, significant distortions to the data may occur, and a complex system of corrections should be developed and implemented [68]. In addition, the corrections for the atmospheric phenomena, illumination, surface inclination angle, etc. have to be made [69]. These issues require the development of the new methods of searching for minerals. These methods can be based on the employment of mobile platforms for collecting heterogeneous data, computer vision systems, and new search criteria identified by machine learning. So far, the unsolved problem is the development of the criteria for the search for minerals using computer vision, machine learning and UAVs. For example, for oil and gas fields, the search criteria are powerful accumulations of sedimentary rocks limited by deep faults; the composition of rocks is mainly carbonate with traces of bituminosity. For copper-porphyry copper deposits, the most important factor of the regional forecast is the substrate in which ore-magmatic systems are located. They should be tectonic zones and terranes composed of oceanic and island-arc melanocratic formations and metasomatic changes at the boundary of the magmatic body and the host rocks. The possibility of using highly mobile platforms and artificial intelligence methods for reliable identification of search criteria requires further research. The countries of the world spend huge sums of money for exploration works, but the discovered fields do not pay off these expenses. According to MinEx Consulting [70], USD 140 billion was spent between 2005 and 2014, while the value of discovered mineral resources was USD 93 billion only.