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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1270/jsbbs.21073
|2 doi
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|a eng
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| 100 |
1 |
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|a Kameoka, Taishin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Assessing streetscape greenery with deep neural network using Google Street View
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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| 500 |
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|a Date Revised 07.09.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Copyright © 2022 by JAPANESE SOCIETY OF BREEDING.
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|a The importance of greenery in urban areas has traditionally been discussed from ecological and esthetic perspectives, as well as in public health and social science fields. The recent advancements in empirical studies were enabled by the combination of 'big data' of streetscapes and automated image recognition. However, the existing methods of automated image recognition for urban greenery have problems such as the confusion of green artificial objects and the excessive cost of model training. To ameliorate the drawbacks of existing methods, this study proposes to apply a patch-based semantic segmentation method for determining the green view index of certain urban areas by using Google Street View imagery and the 'chopped picture method'. We expect that our method will contribute to expanding the scope of studies on urban greenery in various fields
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| 650 |
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|a Journal Article
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|a GIS
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| 650 |
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|a Google Street View
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| 650 |
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|a chopped picture method
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| 650 |
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4 |
|a deep learning
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| 650 |
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4 |
|a green view index
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| 650 |
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4 |
|a urban greenery
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| 700 |
1 |
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|a Uchida, Atsuhiko
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sasaki, Yu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ise, Takeshi
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Breeding science
|d 1998
|g 72(2022), 1 vom: 01. März, Seite 107-114
|w (DE-627)NLM098238299
|x 1344-7610
|7 nnas
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| 773 |
1 |
8 |
|g volume:72
|g year:2022
|g number:1
|g day:01
|g month:03
|g pages:107-114
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| 856 |
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|u http://dx.doi.org/10.1270/jsbbs.21073
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