Mapping invasive Lupine on grasslands using UAV images and deep learning
| dc.contributor.author | Wijesingha, Jayan | |
| dc.contributor.author | Schulze-Brüninghoff, Damian | |
| dc.contributor.author | Wachendorf, Michael | |
| dc.date.accessioned | 2024-04-08T11:56:36Z | |
| dc.date.available | 2024-04-08T11:56:36Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Semi-natural grasslands are threatened by invasive species. This study employs high-resolution images captured by an unmanned aerial vehicle (UAV) and deep learning techniques to map Lupine (Lupinus polyphyllus Lindl.) in grasslands, which is one of the most common invasive species in European grasslands. The methodology involves RGB image acquisition, structure from motion processing, canopy height modelling, and deep learning semantic segmentation model development. The resulting models were trained on RGB data, canopy surface height data, and their combination. The models demonstrate high accuracy and efficacy in identifying Lupine distribution. These models offer a valuable tool for continuously monitoring and managing invasive Lupine, with potential applications in similar environments without retraining. The method is beneficial for early-stage invasion detection, facilitating more targeted management efforts for ecologists. | en |
| dc.identifier.doi | 10.18420/giljt2024_27 | |
| dc.identifier.isbn | 978-3-88579-738-8 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43921 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | 44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft | |
| dc.relation.ispartofseries | Lecture Notes in Informatics(LNI) - Proceedings, Volume P - 344 | |
| dc.subject | invasive species | |
| dc.subject | grassland | |
| dc.subject | deep learning | |
| dc.subject | segmentation | |
| dc.subject | UAV images | |
| dc.title | Mapping invasive Lupine on grasslands using UAV images and deep learning | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 466 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 461 | |
| gi.conference.date | 27.-28. Februar 2024 | |
| gi.conference.location | Stuttgart | |
| gi.conference.review | full |
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