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001050633 005__ 20260115203947.0
001050633 0247_ $$2doi$$a10.5194/isprs-annals-X-G-2025-885-2025
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001050633 0247_ $$2ISSN$$a2194-9042
001050633 0247_ $$2ISSN$$a2196-6346
001050633 0247_ $$2datacite_doi$$a10.34734/FZJ-2026-00385
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001050633 1001_ $$0P:(DE-HGF)0$$aTian, Jiaojiao$$b0$$eCorresponding author
001050633 245__ $$aDeep learning based individual tree crown delineation from panchromatic aerial imagery
001050633 260__ $$aRed Hook, NY$$bCurran$$c2025
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001050633 520__ $$aAccurate delineation of individual tree crowns (ITC) enables a better understanding of tree-level growth dynamics and evaluating tree vitality. In recent year, researches have introduced deep learning techniques in this field. However, the precise segmentation relies on high quality annotated dataset and test images with limited domain gaps between the training data. Under the framework of the Helmholtz project, panchromatic airborne images are captured over a mixed European forest. In this research, we adopt a UAV benchmark dataset as training data. To close the domain gaps, a deep learning based colorization step is added, for which two deep learning frameworks are compared to achieve an improved ITC delineation result in a dense forest area.
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001050633 7001_ $$0P:(DE-HGF)0$$aPanangian, Daniel$$b1
001050633 7001_ $$0P:(DE-HGF)0$$aFan, Wen$$b2
001050633 7001_ $$0P:(DE-Juel1)172711$$aSiegmann, Bastian$$b3$$ufzj
001050633 7001_ $$0P:(DE-HGF)0$$aYuan, Xiangtian$$b4
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