Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset

2026-07-11T20:40:17Z https://rodare.hzdr.de/oai2d

oai:rodare.hzdr.de:3668 2026-01-28T11:35:01Z openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data 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true 3.1 HZDR.RODARE 10.14278/rodare.3668 Arbash, Elias 0009-0000-2187-9171 Helmholtz-Institut Freiberg für Ressourcentechnologie - University of Antwerb Afifi, Ahmed Jamal Mohammaed 0000-0001-6782-6753 Belahsen, Ymane National School of Applied Sciences of Oujda Fuchs, Margret 0000-0001-7210-1132 Helmholtz-Institut Freiberg für Ressourcentechnologie Ghamisi, Pedram 0000-0003-1203-741X Helmholtz-Institut Freiberg für Ressourcentechnologie Scheunders, Paul 0000-0003-2447-4772 University of Antwerp Gloaguen, Richard 0000-0002-4383-473X Helmholtz-Institut Freiberg für Ressourcentechnologie Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset Rodare 2025 circular economy automated data processing optical sensors Hyperspectral Imaging HSI Hyperspectral Imaging classification recycling E-waste hyperspectral imaging dataset RGB dataset conveyor belt sensors spectrometers machine learning deep learning Electrolyzers open source digitalization Transformers 2025-04-07 https://rodare.hzdr.de/record/3668 https://www.hzdr.de/publications/Publ-41192 https://www.hzdr.de/publications/Publ-42879 10.14278/rodare.3667 https://rodare.hzdr.de/communities/hzdr https://rodare.hzdr.de/communities/rodare Creative Commons Attribution 4.0 International Open Access Electrolyzers-HSI Dataset Description: The Electrolyzers-HSI dataset is a multiscene RGB-Hyperspectral benchmark dataset comprising 55 scene of shredded Electrolyzers samples. The RGB images are collected using a Teledyne Dalsa C4020 camera on a conveyor belt, while hyperspectral images (HSI) are acquired with a FENIX spectrometer. The HSI data contains 450 bands in the VNIR and SWIR range [400 - 2500]nm. Data Format <ul> <li>RGB Images: .jpg files</li> <li>Ground Truth (GT): .png files. They appear black since the values are between 0 and 5. Correct visualization is done via script.</li> <li>HSI Data: Each hyperspectral data cube .img file is accompanied by a .hdr file.</li> </ul> Folder Organization <ul> <li>Electrolyzers-HSI: 55 subfolders <ul> <li>1/ <ul> <li>’GT.png’ file for segmentation ground truth</li> <li>‘HSI.img’ and ‘HSI.hdr’ files for HSI data cube</li> <li>‘RGB.jpg’ file for the RGB image</li> </ul> </li> <li>2/ <ul> <li>’GT.png’ file for segmentation ground truth</li> <li>‘HSI.img’ and ‘HSI.hdr’ files for HSI data cube</li> <li>‘RGB.jpg’ file for the RGB image</li> </ul> </li> <li>3/4/5/6/ … :Same structure for all rest of folders</li> </ul> </li> </ul> Data Classes in Masks <ul> <li>Masks contain 0 to 5 segmentation classes: <ul> <li>0: background</li> <li>1: “MESH”</li> <li>2: “Steel_Cathode”</li> <li>3: "Steel_Anode”</li> <li>4: “HTEL_Anode”</li> <li>5: “HTEL_Cathode”</li> </ul> </li> </ul> Code Repository To facilitate reading and working with the data, Python codes are available on the GitHub repository: https://github.com/hifexplo Citation If you use this dataset, please cite the following article: Word: Latex: