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---
license: mit
---
<div align="center">
<h1 style="font-size: 2.8em; margin-bottom: 0.3em;">
E-SCDD Dataset
</h1>
<a href="https://github.com/sntubix/pv-ddpm.git" target="_blank">
<img alt="GitHub Repo"
src="https://img.shields.io/badge/GitHub-e--scdd-000000?logo=github&logoColor=white" />
</a>
<a href="https://arxiv.org/abs/2511.09604" target="_blank">
<img alt="arXiv"
src="https://img.shields.io/badge/arXiv-2511.09604-b31b1b?logo=arxiv&logoColor=white" />
</a>
</div>
## Overview
**E-SCDD** is an extended electroluminescence (EL) imagery dataset for photovoltaic (PV) module analysis.
It expands the existing [BenchmarkELimages](https://github.com/TheMakiran/BenchmarkELimages) dataset by adding additional EL samples,
refined pixel-level annotations, and standardized splits to support research on:
- defect detection
- anomaly segmentation
- robustness evaluation
- generative modeling
- PV modules' health monitoring and maintenance
## Dataset Structure
- Images: EL imagery of PV modules, including multiple defect types such as cracks and inactive regions.
- Annotations: pixel-level segmentation masks provided in indexed PNG format.
- Splits: train, validation, and test folders following the original schema
## Citation
This dataset extends and builds upon the [BenchmarkELimages](https://github.com/TheMakiran/BenchmarkELimages) dataset,
originally released under the MIT License.
Please cite both works when using this dataset.
```
@misc{hanifi2025bridgingdatagapspatially,
title={Bridging the Data Gap: Spatially Conditioned Diffusion Model for Anomaly Generation in Photovoltaic Electroluminescence Images},
author={Shiva Hanifi and Sasan Jafarnejad and Marc Köntges and Andrej Wentnagel and Andreas Kokkas and Raphael Frank},
year={2025},
eprint={2511.09604},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2511.09604},
}
@article{pratt2023benchmark,
title={A benchmark dataset for defect detection and classification in electroluminescence images of PV modules using semantic segmentation},
author={Pratt, Lawrence and Mattheus, Jana and Klein, Richard},
journal={Systems and Soft Computing},
pages={200048},
year={2023},
publisher={Elsevier} }
``` |