Datasets & Opensources

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 

Title: Remote Sensing-Enhanced Spatio-Temporal Model for Precision Prediction of Anopheles Larval Habitats

Prepared by:
Fahimeh Youssefi
Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University, Tehran, Iran.
Institute of Artificial Intelligence, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing, Zhejiang Province, Postal Code 312000, China.
youssefi@email.kntu.ac.ir
youssefi@email.usx.edu.cn 
DOI: 10.5281/zenodo.11081690

Visit https://zenodo.org/records/11081690?token=eyJhbGciOiJIUzUxMiIsImlhdCI6MTcxNDM2MDkxMSwiZXhwIjoxNzk4NTg4Nzk5fQ.eyJpZCI6IjczNjE2MWU4LTg4NmMtNGJlNC1hZjQwLWIwM2I4ZTI5M2EwNCIsImRhdGEiOnt9LCJyYW5kb20iOiJiMTAyNGQ1YmNmOTcwMTRmZGZlODE1YzVkN2YzNjg3MCJ9.rmW7FluXlaeyd0Y4SaWCciXz0fCbj6HoKFT5c53QWireTJVmQXrUG18eULSdeUBsJnSTIjfCycFTRGFrRiGurA
for datasets and more information.

Title: Landslide Detection: Faster-RCNN

Prepared by:
Southwest Jiaotong University: Team & Saied Pirasteh
email: sapirasteh1@usx.edu.cn

Visit https://zenodo.org/records/11081771?token=eyJhbGciOiJIUzUxMiIsImlhdCI6MTcxNDM2NDYyNSwiZXhwIjoxNzk4NTg4Nzk5fQ.eyJpZCI6IjVlOThiM2QzLTRiNGYtNDI4NC1iNjhiLWM0Zjk5MDdiZmMzNyIsImRhdGEiOnt9LCJyYW5kb20iOiIwMTgyNDQ3M2Q4YzY2M2Y3NGQ3Y2U5MTgyMjY3Yzk1MyJ9.OxfDyJJOhYvu9951-IufDn9sbt1B7ylTL-M1Zfy0oTtefTH2S_K5VxyacBVBvrjbmSGE7nGnRfNhuYi6ExH6nQ
for dataset and code.

 

 

 

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The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.

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