Stark, Thomas und Pardo, Julia und Wurm, Michael und Leichtle, Tobias und Martin, Klaus und Taubenböck, Hannes (2025) Urban Tree Detection: Comparing YOLOv8 and DeepForest for Accurate Single-Tree Identification from Aerial Imagery. In: 2025 Joint Urban Remote Sensing Event, JURSE 2025, Seiten 1-4. IEEE. 2025 Joint Urban Remote Sensing Event (JURSE), 2025-05-05 - 2025-05-07, Tunis. doi: 10.1109/JURSE60372.2025.11076000. ISBN 979-8-3503-7183-3. ISSN 2642-9535.
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Offizielle URL: https://ieeexplore.ieee.org/document/11076000
Kurzfassung
This study addresses the need for accurate tree inventory monitoring in urban areas to support planning and environmental efforts, by leveraging advances in deep learning for single tree detection. Using very high resolution aerial images of a residential area in Munich, we compared the performance of two object detection algorithms, YOLOv8 and DeepForest, on the urban tree detection task. The ground truth data consisted of hand drawn tree bounding boxes, and the performance was monitored mostly by F1 score measures. Subsequently, efforts concentrated on improving DeepForest’s performance through fine-tuning. The enhancements yielded a notable increase in the model’s F1 score from 0.7365 to 0.8030, indicating the effectiveness of these techniques. These findings further underline the potential of deep learning for urban tree detection and highlight the viability of models like DeepForest for creating accurate urban tree inventories, with promising avenues for future enhancement.
elib-URL des Eintrags: | https://elib.dlr.de/215454/ | ||||||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||||||||||||||
Titel: | Urban Tree Detection: Comparing YOLOv8 and DeepForest for Accurate Single-Tree Identification from Aerial Imagery | ||||||||||||||||||||||||||||
Autoren: |
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Datum: | 16 Juli 2025 | ||||||||||||||||||||||||||||
Erschienen in: | 2025 Joint Urban Remote Sensing Event, JURSE 2025 | ||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||||||
DOI: | 10.1109/JURSE60372.2025.11076000 | ||||||||||||||||||||||||||||
Seitenbereich: | Seiten 1-4 | ||||||||||||||||||||||||||||
Verlag: | IEEE | ||||||||||||||||||||||||||||
ISSN: | 2642-9535 | ||||||||||||||||||||||||||||
ISBN: | 979-8-3503-7183-3 | ||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||
Stichwörter: | Deep Learning, Object Detection, Single Tree Detection, Urban Tree | ||||||||||||||||||||||||||||
Veranstaltungstitel: | 2025 Joint Urban Remote Sensing Event (JURSE) | ||||||||||||||||||||||||||||
Veranstaltungsort: | Tunis | ||||||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||||||
Veranstaltungsbeginn: | 5 Mai 2025 | ||||||||||||||||||||||||||||
Veranstaltungsende: | 7 Mai 2025 | ||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Fernerkundung u. Geoforschung | ||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit | ||||||||||||||||||||||||||||
Hinterlegt von: | Stark, Thomas | ||||||||||||||||||||||||||||
Hinterlegt am: | 31 Jul 2025 08:30 | ||||||||||||||||||||||||||||
Letzte Änderung: | 04 Sep 2025 14:27 |
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