Mrs. Dinara Talgarbaeva | Remote Sensing | Top Researcher Award
Mrs. Dinara Talgarbaeva | Remote Sensing | Institute of Ionosphere | Kazakhstan
Mrs. Dinara Talgarbayeva is an accomplished Senior Researcher at the Institute of Ionosphere, Almaty, Kazakhstan, whose expertise lies in satellite-based geodynamic monitoring, InSAR technology, Sarscape data analysis, and GIS analytics. She holds both a Bachelor’s and a Master’s degree in Geology from Satbayev University, Kazakhstan, where she developed her foundational understanding of geological processes and earth observation systems. Over the course of her career, Mrs. Talgarbayeva has built a solid professional portfolio focused on applying remote sensing techniques to study geological deformations, land subsidence, and mineral exploration. Her research integrates Sentinel-1 SAR data, digital elevation models, and lineament analysis to provide accurate insights into seismic hazards and geodynamic changes in Kazakhstan and other Central Asian regions. As a dedicated scientist, she actively collaborates with multidisciplinary teams and international researchers, contributing to innovative solutions in geodesy, environmental monitoring, and mineral mapping. Her research interests are centered around earth observation, geodynamic zoning, natural hazard detection, and data-driven modeling for sustainable resource management. Mrs. Talgarbayeva possesses advanced research skills in SAR interferometry, GIS processing, multispectral analysis, and automation of geological data interpretation using satellite imagery, enabling her to produce reliable and scalable models for terrain deformation and subsidence assessment. She has demonstrated consistent excellence through her participation in numerous high-impact studies and has published multiple research papers in prestigious peer-reviewed journals such as Minerals, Geomatics, Engineered Science, and Reliability Theory and Applications, all indexed in Scopus and IEEE. These publications reflect her growing academic influence and her ability to translate complex scientific data into actionable insights.



















Satellite imagery for rapid detection of liquefaction surface manifestations: Türkiye–Syria 2023 Earthquakes – Remote Sensing, 2023, Cited by: 32
Automated 3D jointed rock mass structural analysis using LiDAR for rockfall susceptibility – Geotechnical and Geological Engineering, 2020, Cited by: 29
Evaluation of machine learning algorithms for object-based mapping of landslide zones using UAV data – Geosciences, 2021, Cited by: 26
3D hazard analysis and object-based characterization of landslide motion using UAV imagery – International Archives of Photogrammetry and Remote Sensing, 2019, Cited by: 20