Datasets & Opensources

Commissions

Leafmap

#Leafmap now supports downloading Google Open Buildings for any country with only one line of code. It can automatically download all tiles and merge them as a single vector file.

BloodStain_Identification_using_ViT

This model refers to a Hybrid Vision Transformer for Hyperspectral Imaging-based Bloodstain Classification, which was developed by Mr. Muhammad Hassaan Farooq Butt.

For more information, please visit: https://github.com/MHassaanButt/BloodStain_Identification_using_ViT.

Capacity Building and Education

Datasets for the Shaoxing University student achievement dataset and the MIT-BIH Arrhythmia database. Developed by Associate Professor Sheng Feng, Institute of Artificial Intelligence, Shaoxing University.

For more information or downloading the datasets, please visit: https://github.com/fengsheng13/datasets

Project: Water Segmentation Datasets

Prepared by:
Armin Moghimi
Ludwig Franzius Institute of Hydraulic, Estuarine and Coastal Engineering
Leibniz University Hannover

For more information, please visit:
https://www.kaggle.com/datasets/arminmoghimi/lufi-riversnap

Project: Tensor-based Relative Radiometric Normalization (RRN) and its optimization using TRR and GA (codes and dataset)

Prepared by:
Armin Moghimi
Ludwig Franzius Institute of Hydraulic, Estuarine and Coastal Engineering
Leibniz University Hannover
moghimi@lufi.uni-hannover.de

Link for Tensor-based-keypoint-detection
https://github.com/ArminMoghimi/Tensor-based-keypoint-detection

Keypoint-based Relative Radiometric Normalization (RRN)

Prepared by:
Armin Moghimi
Ludwig Franzius Institute of Hydraulic, Estuarine and Coastal Engineering
Leibniz University Hannover
moghimi@lufi.uni-hannover.de

For more information, please visit: https://github.com/ArminMoghimi/keypoint-based-RRN

The Edge-Aware MRF (EAMRF) Forest Change Detection Method

Prepared by:
Armin Moghimi
Ludwig Franzius Institute of Hydraulic, Estuarine and Coastal Engineering
Leibniz University Hannover
moghimi@lufi.uni-hannover.de

For more information, please visit: https://github.com/ArminMoghimi/The-EAMRF-forest-change-detection-method