Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset.
Elias Arbash, Ahmed Jamal Afifi, Ymane Belahsen, Margret Fuchs, Pedram Ghamisi, Paul Scheunders, Richard Gloaguen
Abstract
Open AccessThe global challenge of sustainable recycling demands automated, fast, and accurate material detection systems that act as a bedrock for a circular economy. Integrating front-tier technologies into advanced recycling systems democratizes access to AI-driven sustainability, and transforms waste analysis from isolated research efforts into real-time, scalable industrial practice. This integration not only accelerates material recovery but also strengthens the technological backbone required to achieve large-scale recycling and alignment with the Green Deal ambitions. In response, we introduce Electrolyzers-HSI, a new multimodal benchmark dataset designed to accelerate the recovery of critical raw materials through accurate electrolyzer materials classification. The dataset comprises 55 co-registered high-resolution RGB images and hyperspectral imaging (HSI) data cubes spanning the 400-2500 nm spectral range. This enables non-invasive analysis of shredded electrolyzer samples, facilitating quantitative material classification. We evaluate various analytical methods, including state-of-the-art (SOTA) Transformer-based deep learning (DL) architectures, to validate the dataset for robust electrolyzers identification. The openly accessible dataset and codebase promote reproducible research and facilitate broader adoption of smart and sustainable E-waste recycling.