Multi-Resolution Segmentation of Solar Photovoltaic Systems Using Deep Learning
| dc.date.accessioned | 2024-01-24T13:29:59Z | |
| dc.date.available | 2024-01-24T13:29:59Z | |
| dc.date.issued | 2023-12-11 | |
| dc.description.sponsorship | Gefördert durch den Publikationsfonds der Universität Kassel | |
| dc.identifier | doi:10.17170/kobra-202401249423 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/15394 | |
| dc.language.iso | eng | |
| dc.relation.doi | doi:10.3390/rs15245687 | |
| dc.rights | Namensnennung 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | solar photovoltaic systems | eng |
| dc.subject | photovoltaic plants | eng |
| dc.subject | remote sensing | eng |
| dc.subject | machine learning | eng |
| dc.subject | deep learning | eng |
| dc.subject | object detection | eng |
| dc.subject | image segmentation | eng |
| dc.subject.ddc | 600 | |
| dc.subject.swd | Fotovoltaik | ger |
| dc.subject.swd | Deep learning | ger |
| dc.subject.swd | Erneuerbare Energien | ger |
| dc.subject.swd | Maschinelles Lernen | ger |
| dc.subject.swd | Bildsegmentierung | ger |
| dc.subject.swd | Objekterkennung | ger |
| dc.title | Multi-Resolution Segmentation of Solar Photovoltaic Systems Using Deep Learning | eng |
| dc.type | Aufsatz | |
| dc.type.version | publishedVersion | |
| dcterms.abstract | In the realm of solar photovoltaic system image segmentation, existing deep learning networks focus almost exclusively on single image sources both in terms of sensors used and image resolution. This often prevents the wide deployment of such networks. Our research introduces a novel approach to train a network on a diverse range of image data, spanning UAV, aerial, and satellite imagery at both native and aggregated resolutions of 0.1 m, 0.2 m, 0.3 m, 0.8 m, 1.6 m, and 3.2 m. Using extensive hyperparameter tuning, we first determined the best possible parameter combinations for the network based on the DeepLabV3 ResNet101 architecture. We then trained a model using the wide range of different image sources. The final network offers several advantages. It outperforms networks trained with single image sources in multiple test applications as measured by the F1-Score (95.27%) and IoU (91.04%). The network is also able to work with a variety of target imagery due to the fact that a diverse range of image data was used to train it. The model is made freely available for further applications. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Kleebauer, Maximilian | |
| dcterms.creator | Marz, Christopher | |
| dcterms.creator | Reudenbach, Christoph | |
| dcterms.creator | Braun, Martin | |
| dcterms.extent | 21 Seiten | |
| dcterms.source.articlenumber | 5687 | |
| dcterms.source.identifier | eissn:2072-4292 | |
| dcterms.source.issue | Issue 24 | |
| dcterms.source.journal | Remote Sensing | eng |
| dcterms.source.volume | Volume 15 | |
| kup.iskup | false |