@inproceedings{f78cdc1224f9402bbb81b1fe98012480,
title = "Mining Satellite Imagery for Offshore Wind Energy",
abstract = "This work mines big data in Sentinel-1 satellite images to unveil geographical patterns in offshore wind energy. We leverage unsupervised machine learning to extract insights from a 44GB open access dataset for decision support in wind farm orientations to guide stakeholders. It has broader impacts of overcoming climate change by enhancing renewable energy.",
keywords = "Climate Change, Clustering, Geospatial Big Data, Image Mining, Ocean Wind Field, Radar, Renewable Energy, Satellite Data, Unsupervised Learning, World Geodetic System",
author = "Monsalve, {Cristian C.Noriega} and Varde, {Aparna S.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Big Data, BigData 2024 ; Conference date: 15-12-2024 Through 18-12-2024",
year = "2024",
doi = "10.1109/BigData62323.2024.10825812",
language = "English",
series = "Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8778--8780",
editor = "Wei Ding and Chang-Tien Lu and Fusheng Wang and Liping Di and Kesheng Wu and Jun Huan and Raghu Nambiar and Jundong Li and Filip Ilievski and Ricardo Baeza-Yates and Xiaohua Hu",
booktitle = "Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024",
}
