# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 3 Limitations in the Process of IUAVT Adaptation in the Sectors of Economy  
Although the literature presents the solutions for the use of UAVs in various industries [14], however, at present, IUAVT still has a number of limitations: **technical** ones (limited battery capacity, flight time, payload, sensor sensitivity, dependence on weather conditions, etc. ), **legal** ones (the impossibility of some use cases within the city), and **software-algorithmic** limitations in planning, flight management, and data processing. **The technical limitations** listed above affect the different extents of performing the tasks with UAVs usage. When solving tasks outside the populated areas, the limited battery capacity and flight time can significantly complicate the use of UAVs for MoWAL, EoM, and MoHGP. A number of tasks of PA, MoEP, and MoTES require the use of special sensors and cameras, as well as motion control in a closed space and in the absence of a GPS signal. **Legal restrictions** are related to the fact that, in many countries, the use of UAVs is legally restricted, especially within the cities. For example, according to the existing legislation of the UAV [110]:  
1. During take-off and landing, it cannot be closer than 50 m horizontally from any person (except for the person operating an unmanned aerial vehicle), another vehicle, building or structure;
2. During the flight, it cannot be closer than 100 m horizontally from any person (except for the person operating the unmanned aerial vehicle), another vehicle, building, or structure;
3. In all cases, closer than 150 m horizontally from a mass gathering of people and (or) vehicles;
4. In restricted and dangerous flight zones, or flight restriction zones. Additional restrictions are imposed on the use of heavy UAVs. **Software and algorithmic limitations** include restrictions in data processing and limitations of software tools and algorithms for UAV control and flight planning. **Data processing. ** The data collected by UAVs in a wide spectral range differ in features characteristic of Big Data [111]: they are heterogeneous, often incomplete, have a large volume, and change quickly. In other words, \"SUAS data are classified as Big\". However, they have additional unique features: multi-dimensionality, high resolution in space and time, significant amounts of the data obtained from a small area, etc. [13]. The peculiarities of these data cause the appearance of specific processing problems, which are addressed by the efforts of the scientific community. The main, but not the only, task of processing data received from the UAV is image processing tasks. **Image processing** includes the tasks of identification (cv1), verification (cv2), recognition (cv3), and determination (cv4) of the visible characteristics of an object (speed, size, distance, etc.). The most successful algorithm for solving cv1 and cv3 problems is the YOLO algorithm [112], which uses a convolutional network to identify the boundaries of an object \"in one pass\". The cv2 problem is often solved using Siamese networks [113], when two images are processed by two identical pre-trained networks. The obtained results (image vectors) are compared using a triplet loss function, which can be implemented as a triplet embedding distance [114] or a triplet of probabilistic embeddings [115]. The quality of solving cv1-4 problems depends on the subject area and data sets (data set -DS). To configure deep neural networks, sets of labeled images with ImageNet [116], Open Images [117], COCO Dataset [118], and FaceNet are widely used. However, the existing DS and deep network architectures do not take into account the peculiarities of the IUAVT solved tasks. Only recently, to solve the specific problems arising from the features of the data received from the UAV:  
1. **Data sets are being developed for training deep networks, taking into account the peculiarities of the use of UAVs. ** For example, [119] describes data sets (nature, fields, hills, forests, roads), including synthetic ones [120];
2.