| id |
caadria2020_028 |
| authors |
Xia, Yixi, Yabuki, Nobuyoshi and Fukuda, Tomohiro |
| year |
2020 |
| title |
Development of an Urban Greenery Evaluation System Based on Deep Learning and Google Street View |
| doi |
https://doi.org/10.52842/conf.caadria.2020.1.783
|
| source |
D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 783-792 |
| summary |
Street greenery has long played a vital role in the quality of urban landscapes and is closely related to people's physical and mental health. In the current research on the urban environment, researchers use various methods to simulate and measure urban greenery. With the development of computer technology, the way to obtain data is more diverse. For the assessment of urban greenery quality, there are many methods, such as using remote sensing satellite images captured from above (antenna, space) sensors, to assess urban green coverage. However, this method is not suitable for the evaluation of street greenery. Unlike most remote sensing images, from a pedestrian perspective, urban street images are the most common view of green plants. The street view image presented by Google Street View image is similar to the captured by the pedestrian perspective. Thus it is more suitable for studying urban street greening. With the development of artificial intelligence, based on deep learning, we can abandon the heavy manual statistical work and obtain more accurate semantic information from street images. Furthermore, we can also measure green landscapes in larger areas of the city, as well as extract more details from street view images for urban research. |
| keywords |
Green View Index; Deep Learning; Google Street View; Segmentation |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (20,804,410 bytes) |
| references |
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