A multi-label sentinel-2 dataset for deep learning-based energy, transport, and storage infrastructure mapping in Germany.
- DOI
- 10.1016/j.dib.2026.113226
- Published
- 2026 Aug
- Container
- Data in brief
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.dib.2026.113226,
title = {A multi-label sentinel-2 dataset for deep learning-based energy, transport, and storage infrastructure mapping in Germany.},
author = {Pasold L and Eisentraut L and Waigner F and Buettner R},
year = {2026},
journal = {Data in brief},
doi = {10.1016/j.dib.2026.113226},
url = {https://doi.org/10.1016/j.dib.2026.113226}
}RIS
TY - JOUR TI - A multi-label sentinel-2 dataset for deep learning-based energy, transport, and storage infrastructure mapping in Germany. AU - Pasold L AU - Eisentraut L AU - Waigner F AU - Buettner R PY - 2026 JO - Data in brief DO - 10.1016/j.dib.2026.113226 UR - https://doi.org/10.1016/j.dib.2026.113226 ER -
APA
L, P., L, E., F, W., & R, B. (2026). A multi-label sentinel-2 dataset for deep learning-based energy, transport, and storage infrastructure mapping in Germany.. Data in brief. https://doi.org/10.1016/j.dib.2026.113226
Source records
- pubmed · retrieved 2026-09-25T06:28:19.831Z