Open-source ML platform for detecting deforestation, ice melt, and flooding from Sentinel-2 / Landsat imagery.

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

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.

This is a joint project of Vinit Firke (myself) and Narunat Pantapalin (https://github.com/narupanta) for the Fortgeschrittene Praktikum Data Science course happened in Winter Semester 2024-25 at TU Braunschweig.

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.

End-to-end deep learning pipeline for melanoma detection using U-Net segmentation and EfficientNet classification.

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.

Satellite imagery analysis using deep learning

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

A fully automated, high-fidelity, and multi-input GAN-based framework for precise cloud removal from satellite images with enhanced interpretability and performance across varying cloud types and terrains.

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).

Deep learning model for automatic lung segmentation from chest X-ray images using U-Net architecture with ResNet34 encoder.

Plataforma inteligente