Yasser Ebrahimian Ghajari | Geo-Informatics | Best Researcher Award

Assist. Prof. Dr. Yasser Ebrahimian Ghajari | Geo-Informatics | Best Researcher Award 

Assistant Professor | Babol Noshirvani University of Technology | Iran

Dr. Yasser Ebrahimian Ghajari is a distinguished scholar, educator, and leader in the field of geomatics, with expertise spanning surveying engineering, GIS, remote sensing, spatial data analysis, and knowledge-based technology development. He holds a Ph.D. in Surveying Engineering (GIS Track), an M.Sc. in Civil Engineering specializing in Surveying and GIS, and a B.Sc. in Surveying Engineering, all achieved with top academic rankings. Professionally, he serves as a faculty member at Babol Noshirvani University of Technology and has held prominent leadership roles as founder and director of national-level geomatics initiatives, head of scientific associations, and board member in engineering and mining organizations. His research interests include geospatial data modeling, GNSS systems, AI-driven spatial applications, disaster and crisis management, urban and environmental analysis, land and property management, and smart infrastructure monitoring. He has supervised and executed numerous large-scale national and regional projects across sectors such as water resources, transportation, agriculture, disaster management, and urban planning, while authoring nearly 50 peer-reviewed publications and serving as a reviewer and keynote speaker in many scientific forums. Recognized as a top researcher and elite member in Iran, his awards and honors reflect his excellence in both academic and applied contributions to geomatics and civil engineering. In conclusion, Dr. Ebrahimian Ghajari’s career demonstrates a unique integration of academic achievement, professional leadership, and impactful research, positioning him as a leading authority in advancing geomatics technologies for sustainable development and societal benefit.

Profile: Google Scholar

Featured Publications

Ebrahimian Ghajari, Y., Alesheikh, A. A., Modiri, M., Hosnavi, R., & Abbasi, M. (2017). Spatial modelling of urban physical vulnerability to explosion hazards using GIS and fuzzy MCDA. Sustainability, 9(7), 1274

Ebrahimian Ghajari, Y., Alesheikh, A. A., Modiri, M., Hosnavi, R., Abbasi, M., & Sharifi, A. (2018). Urban vulnerability under various blast loading scenarios: Analysis using GIS-based multi-criteria decision analysis techniques. Cities, 72, 102–114.

Ahmadlou, M., Ebrahimian Ghajari, Y., & Karimi, M. (2022). Enhanced classification and regression tree (CART) by genetic algorithm (GA) and grid search (GS) for flood susceptibility mapping and assessment. Geocarto International, 37(26), 13638–13657.

Ebrahimian Ghajari, Y., & Barari Siavoshkolaei, M. (2019). Runoff production potential zoning using fuzzy GIS-MCDA models (case study: Tajan river basin). Journal of Geomatics Science and Technology, 9(1), 1–14.

Amirsoleymani, Y., Abessi, O., & Ebrahimian Ghajari, Y. (2022). A spatial decision support system for municipal solid waste landfill sites (case study: The Mazandaran Province, Iran). Waste Management & Research, 40(7), 940–952.

Ebrahimian-Ghajari, Y., Alesheikh, A. A., Modiri, M., Hosnavi, R., & Nekouei, M. A. (2016). Modeling of seismic vulnerability of urban buildings in geographic information system environment: A case study in Babol, Iran.

 

Meng Jia | Remote Sensing | Best Researcher Award

Assist. Prof. Dr. Meng Jia | Remote Sensing | Best Researcher Award

Associate Professor at Xi’an University of Technology, China.

Dr. Meng Jia  is an Associate Professor at the School of Computer Science and Engineering, Xi’an University of Technology 🇨🇳. She earned her Ph.D. in Pattern Recognition and Intelligent Systems from Xidian University. Her research focuses on remote sensing image change detection, heterogeneous data analysis, and deep learning. She has led multiple national and provincial research projects and has published in top-tier journals like IEEE Transactions on Geoscience and Remote Sensing 📚. With strong expertise in SAR and hyperspectral image processing, she actively contributes to technological advancements in intelligent image understanding 📡.

Professional Profile

Scopus
Google Scholar

Education & Experience

🎓 Education:
  • 📘 Ph.D. in Pattern Recognition and Intelligent Systems, Xidian University (2011–2016)

  • 📘 M.S. in Circuits and Systems, Xidian University (2006–2009)

  • 📘 B.S. in Measurement and Control Technology and Instruments, Xidian University (2002–2006)

💼 Experience:
  • 👩‍🏫 Associate Professor, Xi’an University of Technology (2024–Present)

  • 👩‍🏫 Lecturer, Xi’an University of Technology (2017–2023)

Summary Suitability

Dr. Meng Jia, Associate Professor at the School of Computer Science and Engineering, Xi’an University of Technology, is a highly deserving candidate for the Best Researcher Award for her outstanding contributions to the field of remote sensing, image change detection, and deep learning. With a solid academic foundation, including a Ph.D. in Pattern Recognition and Intelligent Systems from Xidian University, Dr. Jia has consistently demonstrated research excellence, innovation, and national-level impact.

Professional Development 

Dr. Meng Jia continues to grow professionally through active involvement in cutting-edge research projects funded by NSFC and local R&D programs 📊. She regularly attends national and international academic conferences 🌐, expanding her collaboration network and staying current with global trends in AI and remote sensing 📡. Meng contributes to peer-reviewed journals and serves as a reviewer for scientific publications 📝. She also mentors students and junior researchers, promoting academic excellence and innovation in the field of computer vision and geoscience 🌍. Her dedication to continuous learning and collaboration ensures she remains at the forefront of her field 🚀.

Research Focus 

Dr. Meng Jia’s research is centered on intelligent image analysis using deep learning 🤖. Her core focus lies in remote sensing image change detection 🛰️, especially across heterogeneous data sources such as optical, SAR, and hyperspectral imagery 📷. She develops algorithms that improve the accuracy and interpretability of change detection tasks, even with limited labels or unsupervised methods 🧠. Her work often involves multi-modal fusion, feature learning, and knowledge-data driven approaches 🔍. These techniques are applied to monitor environmental changes, urban expansion, and disaster impact assessment 🌍. Her research blends AI innovation with real-world geoscience applications ⚙️.

Awards and Honors

🏅 While specific awards are not listed in the provided data, based on her role and accomplishments, typical honors may include:

  • 🏆 Research project grants from NSFC

  • 🏅 Principal Investigator for multiple provincial-level projects

  • 🧪 Recognition for contributions to intelligent image analysis

  • 📜 Publications in top-tier journals (e.g., IEEE Transactions)

  • 🏅 Academic promotion from Lecturer to Associate Professor

Publication Top Notes

  • Fuzzy Clustering with a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images
    🖊️ Authors: M. Gong, L. Su, M. Jia, W. Chen
    📘 Journal: IEEE Transactions on Fuzzy Systems
    📅 Year: 2013
    📄 Volume: 22, Issue 1, Pages: 98–109
    📊 Citations: 339

  • SAR Change Detection Based on Intensity and Texture Changes
    🖊️ Authors: M. Gong, Y. Li, L. Jiao, M. Jia, L. Su
    📘 Journal: ISPRS Journal of Photogrammetry and Remote Sensing
    📅 Year: 2014
    📄 Volume: 93, Pages: 123–135
    📊 Citations: 115

  • Iterative Training Sample Augmentation for Enhancing Land Cover Change Detection Performance with Deep Learning Neural Network
    🖊️ Authors: Z. Lv, H. Huang, W. Sun, M. Jia, J.A. Benediktsson, F. Chen
    📘 Journal: IEEE Transactions on Neural Networks and Learning Systems
    📅 Year: 2023
    📊 Citations: 66

  • Novel Class-Relativity Non-Local Means with Principal Component Analysis for Multitemporal SAR Image Change Detection
    🖊️ Authors: M. Jia, L. Wang
    📘 Journal: International Journal of Remote Sensing
    📅 Year: 2018
    📄 Volume: 39, Issue 4, Pages: 1068–1091
    📊 Citations: 24

  • Detecting Changes of the Yellow River Estuary via SAR Images Based on a Local Fit-Search Model and Kernel-Induced Graph Cuts
    🖊️ Authors: M. Gong, M. Jia, L. Su, S. Wang, L. Jiao
    📘 Journal: International Journal of Remote Sensing
    📅 Year: 2014
    📄 Volume: 35, Issues 11–12, Pages: 4009–4030
    📊 Citations: 21

  • The Generalized Gamma-DBN for High-Resolution SAR Image Classification
    🖊️ Authors: Z. Zhao, L. Guo, M. Jia, L. Wang
    📘 Journal: Remote Sensing
    📅 Year: 2018
    📄 Volume: 10, Issue 6, Article No.: 878
    📊 Citations: 16

  • Bipartite Graph Attention Autoencoders for Unsupervised Change Detection Using VHR Remote Sensing Images
    🖊️ Authors: M. Jia, C. Zhang, Z. Zhao, L. Wang
    📘 Journal: IEEE Transactions on Geoscience and Remote Sensing
    📅 Year: 2022
    📄 Volume: 60, Pages: 1–15
    📊 Citations: 14

Conclusion

Dr. Meng Jia exemplifies academic leadership, scientific innovation, and societal impact through her research in remote sensing and artificial intelligence. Her work has not only contributed to scientific advancement but also addressed critical global issues such as environmental change detection and resource monitoring. She is a standout candidate for the Best Researcher Award, and her continued contributions are sure to shape the future of intelligent geospatial technologies.