Complex of urban ecological support (CUES – BR21881908 )

Program Goal

The program goal is formulated by the Committee of Science of the Republic of Kazakhstan and defined in Scientific and Technical Task No. 58: “Creation of a complex of ecological support of urban environment on the basis of UAV, which is able in semi-automatic mode to detect air, water pollution, noise, to assess the level of traffic, to identify places of increased heat energy consumption and to map the obtained data”


According to the Tender Documentation, the tasks (1-5) formulated by the Customer in Tender Documentation No. 58 (Appendix A):

Task 1: Development of an unmanned aerial system software and hardware platform to perform monitoring tasks within the urban economy (Unmanned Aerial Programming and Hardware Platform to perform monitoring tasks within the city economy (UARHPC))

Task 2: Development of a set of on-board mounted systems for solving the urban economy monitoring tasks (detection of waste, air and water pollution).

Task 3: Development of software tools for solving the issues of classification and recognition of images and data received from the UAV for solving tasks of mapping of ecological disturbances of air and ground environment of the city, heat losses.

Task 4: Development of a system for displaying the obtained data and the results of their analysis

Task 5 : Development of a set of measures to train the personnel to use the complex and to apply the obtained solutions in the process of training specialists.

Results 2023 год

  • Specifications of requirements for hardware parts of an unmanned aerial system in various configurations have been developed
  • Specifications of requirements for sets of sensors have been developed to solve urban monitoring problems for aircraft platforms of various payloads.


  • Mukhamediev, R.I.; Kuchin, Y.; Popova, Y.; Yunicheva, N.; Muhamedijeva, E.; Symagulov, A.; Abramov, K.; Gopejenko, V.; Levashenko, V.; Zaitseva, E.; et al. Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods. Mathematics 2023, 11, 4687. (CiteScore — Q1, 88%, JCR — Q1, IF: 2.46).
  • Mukhamediev, Ravil I., Timur Merembayev, Yan Kuchin, Dmitry Malakhov, Elena Zaitseva, Vitaly Levashenko, Yelena Popova, Adilkhan Symagulov, Gulshat Sagatdinova, and Yedilkhan Amirgaliyev. Soil Salinity Estimation for South Kazakhstan Based on SAR Sentinel-1 and Landsat-8, 9 OLI Data with Machine Learning Models //Remote Sensing. – 2023. – Т. 15. – №. 17. – С. 4269. ; (Scopus Quartile: Q1, 90%, JCR Category Quartile: Q1, WoS IF=5.0)
  • Mukhamediev, R.I.; Terekhov, A.; Sagatdinova, G.; Amirgaliyev, Y.; Gopejenko, V.; Abayev, N.; Kuchin, Y.; Popova, Y.; Symagulov, A. Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning. Remote Sens. 2023, 15, 5544. (Scopus Quartile: Q1, 90%, JCR Category Quartile: Q1, WoS IF=5.0)