Dr. Bangjie Fu | Geological Engineering | Best Researcher Award 

Ph.D., Central South University, China

Dr. Bangjie Fu is a dedicated researcher at the intersection of Engineering Geology, Remote Sensing Technology, Geographic Information Systems (GIS), and Artificial Intelligence. His expertise lies in statistical modeling and machine learning for geo-hazard analysis, specifically focusing on landslide detection, susceptibility, and risk assessment. Dr. Fu’s research supports proactive geological hazard detection and prevention, contributing to safer and more resilient infrastructures.

Publication Profile

Scopus

Education

Doctorate in Civil Engineering Planning and Management – Central South University. Master’s Degree in Engineering Geology – China University of Geosciences. Bachelor’s Degree in Geology Engineering – Guilin University of Technology

Experience

Dr. Fu’s work includes pioneering applications of AI and machine learning for geo-hazard detection and GIS-based assessments. He has developed and refined models for landslide susceptibility, employing deep learning and data-driven methods to advance the field of geological risk mitigation.

Research Interests

Geo-Hazard Analysis, Landslide Assessment and Detection, Geological Engineering, Remote Sensing Applications in Geo-Hazards.

Publications

“Review on the Artificial Intelligence-based methods in Landslide Detection and Susceptibility Assessment: Current Progress and Future Directions” – International Journal of Geo-Engineering (2024). DOI: 10.1016/j.ige.2024.10.003

“PSO-SLIC algorithm: A novel automated method for the generation of high-homogeneity slope units using DEM data” – Geomorphology (2024). DOI: 10.1016/j.geomorph.2024.109367

“Dynahead-YOLO-Otsu: An efficient DCNN-based landslide semantic segmentation method using remote sensing images” – Journal of Spatial Science (2024). DOI: 10.1080/19475705.2024.2398103

“A side-sampling based Linformer model for landslide susceptibility assessment: A case study of the railways in China” – Journal of Spatial Science (2024). DOI: 10.1080/19475705.2024.2354507

“RIPF-Unet for regional landslides detection: A novel deep learning model boosted by reversed image pyramid features” – Natural Hazards (2023). DOI: 10.1007/s11069-023-06145-0