Research achievement in an international journal classified Q1 in Scopus and Clarivate.

A scientific research paper by faculty member (Assistant Professor Abbas Mohammed Nouri) at Northern Technical University / College of Health and Medical Technologies – Kirkuk was published in the journal Applied Sciences in the first quarter (Q1) in the field of Geomatics Engineering with an impact factor of (2.5) in cooperation with the University of Technology and Al-Isra University.
Research title:
Deep-Learning Integration of CNN–Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
Researchers:
Assistant Professor Abbas Mohammed Nouri (Northern Technical University – College of Health and Medical Technologies – Kirkuk)
Prof. Dr. Abdulrazzaq Taresh Zboun (Al-Isra University – College of Engineering)
Prof. Amjad Nasser Mohsen (University of Technology, College of Civil Engineering)
The research addresses the development of a hybrid deep learning algorithm, known as CCT-U-ViT, which combines the spatial feature extraction of U-Net and the global context capability of the Transformer. The model significantly reduces the number of basic blocks by using CCT instead of the Vision Transformer, making it ideally suited for small-scale flood datasets. This model improves flood boundary identification by incorporating local spatial patterns and global contextual relationships. However, the method relies on Sentinel-1 SAR imagery and focuses on Erbil, Iraq, which experienced severe flash flooding in December2021 .