The aim of the study is to estimate the MALE-class (Medium Altitude Long Endurance) UAV (Unmanned Air Vehicles) using possibility to solve the problem of regular aerial survey of huge areas relative to other means used for this, such as: small-sized UAVs, satellite remote sensing and manned aircrafts. Considered is the issue of practical construction of onboard computer vision system based on a UAV “Orion” wit a ta eoff weig t of more t an a ton, w ic pro ides aerial photography in the visible and near infrared range and airborne laser scanning of the underlying surface with automatic processing of the received data on board in near real-time mode detecting the changes occurred since the previous survey. It has been determined the key components of the computer vision system both the hardware and software platform required highperformance computing and big-data storage. It has been presented a promising architecture, given estimates for its search performance, weight and power consumption, determined the typical flight altitude, which provides the input data spatial resolution, which is necessary for objectoriented change detection algorithms, based on a convolutional neural networks machine learning. It has been proposed organizational and technical solutions to speed up the data processing cycle, taking into account the requirements of the legislation regarding the declassification of aerial survey data. The results obtained confirm that after the issuance of the Orion UAV by the Federal Air Transport Agency of the aircraft type certificate, which gives the right to perform commercial flights in the shared airspace of the Russian Federation, it will be possible to implement an aerial survey complex of high productivity and degree of autonomy using cut of the edge CV & ML technologies. It seems the tactical, technical and economic capabilities of which proposed will be orders of magnitude superior to the currently existing solutions especially for hardto- reach regions.
