Mrs. Dinara Talgarbaeva | Remote Sensing | Top Researcher Award

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.

Professional Profile: ORCID | Scopus

Selected Publications 

  1. Talgarbayeva, D., Satbergenova, A., Vilayev, A., Urazaliyev, A., & Yelisseyeva, A. (2025). InSAR-based assessment of land subsidence induced by coal mining in Karaganda, Kazakhstan. Geomatics, 5(4). [Cited by 12]

  2. Talgarbayeva, D., Serikbayeva, E., Orynbassarova, E., & Sydyk, N. (2025). Application of multispectral data in detecting porphyry copper deposits: The case of Aidarly Deposit, Eastern Kazakhstan. Minerals, 15(9). [Cited by 9]

  3. Talgarbayeva, D., Vilayev, A., Serikbayeva, E., & Ahmadi, H. (2025). Integrated prospectivity mapping for copper mineralization in the Koldar Massif, Kazakhstan. Minerals, 15(8). [Cited by 11]

  4. Talgarbayeva, D., Kairanbayeva, A., Nurakynov, S., & Mitkov, A. (2024). Predictive system for road condition monitoring based on open climate and remote sensing data – A case study with mountain roads. Engineered Science, 8(2). [Cited by 7]

  5. Talgarbayeva, D., Fremd, A., & Gaipova, A. (2023). Possibilities of lineament analysis of DEM SRTM during geodynamic zoning of seismic hazardous territories (on the example of the North-Tien-Shan region). Reliability Theory and Applications, 5(75), 96–110. [Cited by 5]

Mr. Xiaowo Xu | Remote Sensing award | Best Researcher Award

Mr. Xiaowo Xu | Remote Sensing award | Best Researcher Award 

Mr. Xiaowo Xu, University of Electronic Science and Technology of China 

Xiaowo Xu is a Ph.D. candidate in Information and Communication Engineering at the University of Electronic Science and Technology of China (UESTC), where he has been honing his research skills since September 2022. His academic journey began with a Bachelor of Engineering in Electronic Information Engineering from Sichuan University, followed by a Master of Engineering in the same field at UESTC. His research interests focus on deep learning applications, particularly in object categorization, object detection, instance segmentation, and moving object tracking. Currently, he is dedicated to the intelligent interpretation of synthetic aperture radar (SAR) images. Xiaowo has received several prestigious awards, including the 1st Scholarship for Doctoral Candidates and the Special Scholarship for Doctoral Candidates from UESTC, along with an “Honor Academic” Award and the Outstanding Graduate Student Award for the 2022-2023 academic year.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Xiaowo Xu’s research focus on deep learning applications, particularly in object detection, segmentation, and synthetic aperture radar (SAR) image interpretation, positions him well for the Best Researcher Award. His expertise aligns with cutting-edge areas like object categorization and moving object tracking, essential topics in remote sensing and computer vision, which are currently high-impact fields in academia and industry.

Education:

  • Sep. 2022 – Present: Ph.D. candidate in Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC).
  • Sep. 2020 – Sep. 2022: Master of Engineering in Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC).
  • Sep. 2016 – Jun. 2020: Bachelor of Engineering in Electronic Information Engineering, Sichuan University (SCU).

Work and Research Experience:

  • Ph.D. Research (2022 – Present): Xiaowo Xu is currently pursuing a Ph.D. in Information and Communication Engineering at UESTC, focusing on deep learning applications in synthetic aperture radar (SAR) image intelligent interpretation. His research areas encompass object categorization, detection, instance segmentation, and moving object tracking using deep learning techniques.
  • Master’s Research (2020 – 2022): During his master’s studies at UESTC, he deepened his expertise in information and communication engineering, developing skills in Python, MATLAB, and deep learning frameworks like PyTorch and TensorFlow.
  • Academic Communication and Conferences (2022 – Present): Xiaowo Xu has presented his research through posters at prestigious IEEE conferences, including the International Geoscience and Remote Sensing Symposium and the Radar Conference. His work has been showcased internationally, including in the USA, Malaysia, and China.

Publication top Notes:

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

A Group-Wise Feature Enhancement-and-Fusion Network with Dual-Polarization Feature Enrichment for SAR Ship Detection

RBFA-Net: A Rotated Balanced Feature-Aligned Network for Rotated SAR Ship Detection and Classification

A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering

Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images