Assessing streetscape greenery with deep neural network using Google Street View
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...
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| Published in | Breeding Science Vol. 72; no. 1; pp. 107 - 114 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Tokyo
Japanese Society of Breeding
01.01.2022
Japan Science and Technology Agency |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| ArticleNumber | 21073 |
| Author | Uchida, Atsuhiko Kameoka, Taishin Ise, Takeshi Sasaki, Yu |
| Author_xml | – sequence: 1 fullname: Kameoka, Taishin organization: Center for the Promotion of Interdisciplinary Education and Research, Kyoto University – sequence: 2 fullname: Uchida, Atsuhiko organization: Center for the Promotion of Interdisciplinary Education and Research, Kyoto University – sequence: 3 fullname: Sasaki, Yu organization: Center for the Promotion of Interdisciplinary Education and Research, Kyoto University – sequence: 4 fullname: Ise, Takeshi organization: Field Science Education and Research Center, Kyoto University |
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| References_xml | – reference: Ahn, J. and S. Kwak (2018) Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4981–4990. – reference: Ki, D. and S. Lee (2021) Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning. Landsc Urban Plan 205: 103920. – reference: Li, X., C. Zhang, W. Li, R. Ricard, Q. Meng and W. Zhang (2015) Assessing street-level urban greenery using Google Street View and a modified green view index. Urban For Urban Green 14: 675–685. – reference: Noh, H., S. Hong and B. Han (2015) Learning deconvolution network for semantic segmentation. Proc IEEE Int Conf Comput Vis (ICCV), pp. 1520–1528. – reference: QGIS Development Team (2021) QGIS Geographic Information System QGIS Association. https://qgis.org/downloads/. – reference: Hou, L., D. Samaras, T.M. Kurc, Y. Gao, J.E. Davis and J.H. Saltz (2016) Patch-based convolutional neural network for whole slide tissue image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2424–2433. – reference: Guo, Y., Y. Liu, T. Georgiou and M.S. Lew (2018) A review of semantic segmentation using deep neural networks. Int J Multimed Inf Retr 7: 87–93. – reference: Kampffmeyer, M., A. Salberg and R. Jenssen (2016) Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 680–688. – reference: Lecun, Y., L. Bottou, Y. Bengio and P. Haffner (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE 86: 2278–2324. – reference: Bearman, A., O. Russakovsky, V. Ferrari and L. Fei-Fei (2016) What’s the point: Semantic segmentation with point Supervision, In: Leibe, B., J. Matas, N. Sebe and M. Welling (eds.) Lecture Notes in Computer Science, Springer, Cham, pp. 549–565. – reference: Wang, M. and F. Vermeulen (2020) Life between buildings from a street view image: What do big data analytics reveal about neighbourhood organisational vitality? Urban Stud 58: 3118–3139. – reference: Sharifi, A. (2020) Co-benefits and synergies between urban climate change mitigation and adaptation measures: A literature review. Sci Total Environ 750: 141642. – reference: R Core Team (2018) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org. – reference: Ise, T., M. Minagawa and M. Onishi (2018) Classifying 3 moss species by deep learning, using the “chopped picture” method. Open J Ecol 8: 166–173. – reference: Ringland, J., M. Bohm and S.-R. Baek (2019) Characterization of food cultivation along roadside transects with Google Street View imagery and deep learning. Comput Electron Agric 158: 36–50. – reference: Statistics Bureau, Ministry of Internal Affairs and Communications of Japan (2021) Statistics Map of Japan (Statistics GIS). Portal site of official statistics of Japan. https://www.e-stat.go.jp/gis. – reference: Krizhevsky, A., L. Sutskever and G.E. Hinton (2012) Imagenet classification with deep convolutional neural networks. In: Adv Neural Inf Process Syst, pp. 1097–1105. – reference: Li, X. and D. Ghosh (2018) Associations between body mass index and urban “green” streetscape in Cleveland, Ohio, USA. Int J Environ Res Public Health 15: 2186. – reference: Ye, Y., D. Richards, Y. Lu, X. Song, Y. Zhuang, W. Zeng and T. Zhong (2019) Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices. Landsc Urban Plan 191: 103434. – reference: Watanabe, S., K. Sumi and T. Ise (2020) Identifying the vegetation type in Google Earth images using a convolutional neural network: A case study for Japanese bamboo forests. BMC Ecol 20: 65. – reference: Khoreva, A., R. Benenson, J. Hosang, M. Hein and B. Schiele (2017) Simple does it: Weakly supervised instance and semantic segmentation. Proceeding IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 876–885. – reference: Lu, Y., C. Sarkar and Y. Xiao (2018) The effect of street-level greenery on walking behavior: Evidence from Hong Kong. Soc Sci Med 208: 41–49. – reference: Sharma, N., V. Jain and A. Mishra (2018) An Analysis of Convolutional Neural Networks For Image Classification. Procedia Comput Sci 132: 377–384. – reference: Dong, Z., J. Wang, B. Cui, D. Wang and X. Wang (2020) Patch-based weakly supervised semantic segmentation network for crack detection. Constr Build Mater 258: 120291. – reference: Geospatial Information Authority of Japan (2016) Fundamental Geospatial Data [Kiban Chizu Joho]. https://fgd.gsi.go.jp/download/menu.php. – reference: Kingma, D.P. and J. Ba (2015) Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015). – reference: Google LLC (2021) Google Street View. – reference: Ringland, J., M. Bohm, S.-R. Baek and M. Eichhorn (2021) Automated survey of selected common plant species in Thai homegardens using Google Street View imagery and a deep neural network. Earth Sci Inform 14: 179–191. – reference: Hong, A., J.F. Sallis, A.C. King, T.L. Conway, B. Saelens, K.L. Cain and L.D. Frank (2018) Linking green space to neighborhood social capital in older adults: The role of perceived safety. Soc Sci Med 207: 38–45. – reference: Klemm, W., B.G. Heusinkveld, S. Lenzholzer and B. van Hove (2015) Street greenery and its physical and psychological impact on thermal comfort. Landsc Urban Plan 138: 87–98. – reference: Papandreou, G., L.C. Chen, K. Murphy and A.L. Yuille (2015) Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation. Proc IEEE Int Conf Comput Vis (ICCV), pp. 1742–1750. – reference: Yang, J., L. Zhao, J. Mcbrideand and P. Gong (2009) Can you see green? Assessing the visibility of urban forests in cities. 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| SubjectTerms | Artificial neural networks Automation chopped picture method deep learning GIS Google Street View green view index Image segmentation Neural networks Object recognition Public health Research Paper Urban areas urban greenery |
| Title | Assessing streetscape greenery with deep neural network using Google Street View |
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