Mei, Qipeng ; Steier, Janik ; Iwaszczuk, Dorota (2024)
Integrating Crowd-sourced Annotations of Tree Crowns using Markov Random Field and Multispectral Information.
ISPRS Technical Commission II Symposium 2024. Las Vegas, Nevada, USA (11.06.2024 - 14.06.2024)
doi: 10.5194/isprs-archives-XLVIII-2-2024-257-2024
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Benefiting from advancements in algorithms and computing capabilities, supervised deep learning models offer significant advantages in accurately mapping individual tree canopy cover, which is a fundamental component of forestry management. In contrast to traditional field measurement methods, deep learning models leveraging remote sensing data circumvent access limitations and are more cost-effective. However, the efficiency of models depends on the accuracy of the tree crown annotations, which are often obtained through manual labeling. The intricate features of the tree crown, characterized by irregular contours, overlapping foliage, and frequent shadowing, pose a challenge for annotators. Therefore, this study explores a novel approach that integrates the annotations of multiple annotators for the same region of interest. It further refines the labels by leveraging information extracted from multi-spectral aerial images. This approach aims to reduce annotation inaccuracies caused by personal preference and bias and obtain a more balanced integrated annotation.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Mei, Qipeng ; Steier, Janik ; Iwaszczuk, Dorota |
| Art des Eintrags: | Bibliographie |
| Titel: | Integrating Crowd-sourced Annotations of Tree Crowns using Markov Random Field and Multispectral Information |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2024 |
| Ort: | Hannover, Germany |
| Verlag: | Copernicus Publications |
| Reihe: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Band einer Reihe: | 48-2-2024 |
| Kollation: | 7 Seiten |
| Veranstaltungstitel: | ISPRS Technical Commission II Symposium 2024 |
| Veranstaltungsort: | Las Vegas, Nevada, USA |
| Veranstaltungsdatum: | 11.06.2024 - 14.06.2024 |
| DOI: | 10.5194/isprs-archives-XLVIII-2-2024-257-2024 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Benefiting from advancements in algorithms and computing capabilities, supervised deep learning models offer significant advantages in accurately mapping individual tree canopy cover, which is a fundamental component of forestry management. In contrast to traditional field measurement methods, deep learning models leveraging remote sensing data circumvent access limitations and are more cost-effective. However, the efficiency of models depends on the accuracy of the tree crown annotations, which are often obtained through manual labeling. The intricate features of the tree crown, characterized by irregular contours, overlapping foliage, and frequent shadowing, pose a challenge for annotators. Therefore, this study explores a novel approach that integrates the annotations of multiple annotators for the same region of interest. It further refines the labels by leveraging information extracted from multi-spectral aerial images. This approach aims to reduce annotation inaccuracies caused by personal preference and bias and obtain a more balanced integrated annotation. |
| Fachbereich(e)/-gebiet(e): | 13 Fachbereich Bau- und Umweltingenieurwissenschaften 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Geodäsie 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Geodäsie > Fernerkundung und Bildanalyse |
| Hinterlegungsdatum: | 18 Jun 2024 05:43 |
| Letzte Änderung: | 01 Jul 2024 08:20 |
| PPN: | 519474864 |
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