A deep learning pipeline for semantic segmentation of cracks or defects in materials using U-Net architecture with various backbones. This project includes hyperparameter tuning, custom model architectures, and comprehensive data processing utilities.

An in-depth analysis of deep learning models U-Net and DeepLabV3+ in semantic segmentation, highlighting their applications in urban plaing, environmental monitoring, and geographic information systems.

This project focuses on the detection and analysis of UHIs in Hamburg.The core of this project is to leverage multi-source data, including satellite imagery and vector data, with deep learning techniques (specifically U-Net architectures) for high-resolution UHI mapping and analysis.

QGIS Plugin for skidtrail detection from ALS point clouds and deep learning model inference

Using Iris Image Segmentation for Eye Disease Detection, Biometrics, Iris Recognition Systems

U-Net for MRI segmentation.

A project for segmenting buildings in satellite images using the U-Net architecture. Includes data preprocessing, model training, and evaluation scripts, along with preprocessed datasets and trained models.

A comprehensive deep learning project for detecting and segmenting brain diseases, particularly tumors, in MRI scans using multiple state-of-the-art architectures including U-Net and Meta's Segment Anything Model (SAM).