Kameoka Taishin, Uchida Atsuhiko, Sasaki Yu, Ise Takeshi
Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kitashirakawaoiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan.
Field Science Education and Research Center, Kyoto University, Kitashirakawaoiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan.
Breed Sci. 2022 Mar;72(1):107-114. doi: 10.1270/jsbbs.21073. Epub 2022 Feb 25.
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.
城市地区绿化的重要性传统上是从生态、美学角度以及公共卫生和社会科学领域进行讨论的。近期实证研究的进展得益于街景“大数据”与自动图像识别技术的结合。然而，现有的城市绿化自动图像识别方法存在诸如绿色人造物体混淆以及模型训练成本过高的问题。为改善现有方法的缺点，本研究提出应用基于补丁的语义分割方法，通过使用谷歌街景图像和“切块图片法”来确定特定城市区域的绿色视野指数。我们期望我们的方法将有助于扩大各领域城市绿化研究的范围。
