The co-effect of image resolution and crown size on deep learning for individual tree detection and delineation

Individual tree detection and delineation (ITDD) is an important subject in forestry and urban forestry. This study represents the first research to propose the concept of crown resolution to comprehensively evaluate the co-effect of image resolution and crown size on deep learning. Six images with...
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Autor*in:	Zhenbang Hao [verfasserIn] Lili Lin [verfasserIn] Christopher J. Post [verfasserIn] Elena A. Mikhailova [verfasserIn] Kunyong Yu [verfasserIn] Huirong Fang [verfasserIn] Jian Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	mask r-cnn instance segmentation uav image resolution crown-like characteristics

Übergeordnetes Werk:	In: International Journal of Digital Earth - Taylor & Francis Group, 2022, 16(2023), 1, Seite 3753-3771
Übergeordnetes Werk:	volume:16 ; year:2023 ; number:1 ; pages:3753-3771

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1080/17538947.2023.2257636

Katalog-ID:	DOAJ098347373

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callnumber-first	G - Geography, Anthropology, Recreation
author	Zhenbang Hao
spellingShingle	Zhenbang Hao misc GA1-1776 misc mask r-cnn misc instance segmentation misc uav image resolution misc crown-like characteristics misc Mathematical geography. Cartography The co-effect of image resolution and crown size on deep learning for individual tree detection and delineation
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topic	misc GA1-1776 misc mask r-cnn misc instance segmentation misc uav image resolution misc crown-like characteristics misc Mathematical geography. Cartography
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title	The co-effect of image resolution and crown size on deep learning for individual tree detection and delineation
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title_full	The co-effect of image resolution and crown size on deep learning for individual tree detection and delineation
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title_sort	co-effect of image resolution and crown size on deep learning for individual tree detection and delineation
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title_auth	The co-effect of image resolution and crown size on deep learning for individual tree detection and delineation
abstract	Individual tree detection and delineation (ITDD) is an important subject in forestry and urban forestry. This study represents the first research to propose the concept of crown resolution to comprehensively evaluate the co-effect of image resolution and crown size on deep learning. Six images with different resolutions were derived from a DJI Unmanned Aerial Vehicle (UAV), and 1344 manually delineated Chinese fir (Cunninghamia lanceolata (Lamb) Hook) tree crowns were used for six training and validation mask region-based convolutional neural network (Mask R-CNN) models, while additional 476 delineated tree crowns were reserved for testing. The overall detection accuracy, the influence of different crown sizes, and crown resolutions were calculated to evaluate model performance accuracy with different image resolutions for ITDD. Results show that the highest accuracy was achieved when the crown resolution was between 800 and 12800 pixels/tree. The accuracy of ITDD was impacted by crown resolution, and it was unable to effectively identify Chinese fir when the crown resolution was less than 25 pixels/tree or higher than 12800 pixels/tree. The study highlights crown resolution as a critical factor affecting ITDD and suggests selecting the appropriate resolution based on the target detected crown size.
abstractGer	Individual tree detection and delineation (ITDD) is an important subject in forestry and urban forestry. This study represents the first research to propose the concept of crown resolution to comprehensively evaluate the co-effect of image resolution and crown size on deep learning. Six images with different resolutions were derived from a DJI Unmanned Aerial Vehicle (UAV), and 1344 manually delineated Chinese fir (Cunninghamia lanceolata (Lamb) Hook) tree crowns were used for six training and validation mask region-based convolutional neural network (Mask R-CNN) models, while additional 476 delineated tree crowns were reserved for testing. The overall detection accuracy, the influence of different crown sizes, and crown resolutions were calculated to evaluate model performance accuracy with different image resolutions for ITDD. Results show that the highest accuracy was achieved when the crown resolution was between 800 and 12800 pixels/tree. The accuracy of ITDD was impacted by crown resolution, and it was unable to effectively identify Chinese fir when the crown resolution was less than 25 pixels/tree or higher than 12800 pixels/tree. The study highlights crown resolution as a critical factor affecting ITDD and suggests selecting the appropriate resolution based on the target detected crown size.
abstract_unstemmed	Individual tree detection and delineation (ITDD) is an important subject in forestry and urban forestry. This study represents the first research to propose the concept of crown resolution to comprehensively evaluate the co-effect of image resolution and crown size on deep learning. Six images with different resolutions were derived from a DJI Unmanned Aerial Vehicle (UAV), and 1344 manually delineated Chinese fir (Cunninghamia lanceolata (Lamb) Hook) tree crowns were used for six training and validation mask region-based convolutional neural network (Mask R-CNN) models, while additional 476 delineated tree crowns were reserved for testing. The overall detection accuracy, the influence of different crown sizes, and crown resolutions were calculated to evaluate model performance accuracy with different image resolutions for ITDD. Results show that the highest accuracy was achieved when the crown resolution was between 800 and 12800 pixels/tree. The accuracy of ITDD was impacted by crown resolution, and it was unable to effectively identify Chinese fir when the crown resolution was less than 25 pixels/tree or higher than 12800 pixels/tree. The study highlights crown resolution as a critical factor affecting ITDD and suggests selecting the appropriate resolution based on the target detected crown size.
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title_short	The co-effect of image resolution and crown size on deep learning for individual tree detection and delineation
url	https://doi.org/10.1080/17538947.2023.2257636 https://doaj.org/article/3299407ac611419d8023cebdb5df77ec http://dx.doi.org/10.1080/17538947.2023.2257636 https://doaj.org/toc/1753-8947 https://doaj.org/toc/1753-8955
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