| dc.date.accessioned | 2024-10-25T08:24:33Z | |
| dc.date.available | 2024-10-25T08:24:33Z | |
| dc.date.issued | 2024 | |
| dc.identifier | doi:10.17170/kobra-2024102410992 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/16112 | |
| dc.description | Autorenversion; © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ger |
| dc.language.iso | eng | |
| dc.publisher | IEEE | |
| dc.rights | Urheberrechtlich geschützt | |
| dc.rights.uri | https://rightsstatements.org/page/InC/1.0/ | |
| dc.subject | wind turbines | eng |
| dc.subject | renewable energy systems | eng |
| dc.subject | object regression | eng |
| dc.subject | geo-coordinate validation | eng |
| dc.subject.ddc | 600 | |
| dc.title | Enhancing Wind Turbine Location Accuracy: a Deep Learning-Based Object Regression Approach for Validating Wind Turbine Geo-Coordinates | eng |
| dc.type | Konferenzveröffentlichung | |
| dcterms.abstract | Remote sensing and deep learning-based methods can be combined to obtain location information automatically on a large scale. This paper introduces an approach for enhancing the geo-coordinate accuracy of existing wind turbines. By employing a RetinaNet-based method for regressive object localization, turbines can be precisely located in images in addition to being identified. Utilizing semi-automatically processed and manually filtered high-resolution image data, a model is trained with an average precision of 96 %. Subsequently, the model is applied to Germany’s MaStR wind turbine dataset. The application illustrates the advantageous implementation of the method and emphasizes its considerable potential for improving the accuracy of geo-coordinates. While 73.72 % of existing coordinates can be confirmed as correct with a deviation of less than 10 meter, for more than 15 % of the turbine locations coordinates between 10 and 100 meters can be corrected, and for 5.6 % locations a deviation of more than 100 meter can be determined. This showcases the real-world application of the proposed methodology and underscores its significant potential for enhancing the quality of geo-coordinates. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Kleebauer, Maximilian | |
| dcterms.creator | Braun, Axel | |
| dcterms.creator | Horst, Daniel | |
| dcterms.creator | Pape, Carsten | |
| dc.relation.doi | doi:10.1109/IGARSS53475.2024.10641018 | |
| dc.subject.swd | Windturbine | ger |
| dc.subject.swd | Erneuerbare Energien | ger |
| dc.subject.swd | Geoinformation | ger |
| dc.subject.swd | Deep learning | ger |
| dc.subject.swd | Fernerkundung | ger |
| dc.type.version | acceptedVersion | |
| dcterms.event.date | 2024-07-07 | |
| dcterms.event.place | Athen | |
| dcterms.source.collection | IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. Proceedings | eng |
| dcterms.source.identifier | eisbn: 979-8-3503-6032-5 | |
| dcterms.source.identifier | isbn:979-8-3503-6033-2 | |
| dcterms.source.identifier | doi:10.1109/IGARSS53475.2024 | |
| dcterms.source.pageinfo | 7863-7867 | |
| kup.iskup | false | |
| dcterms.event | IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium | eng |
