| id |
caadria2019_396 |
| authors |
Cao, Rui, Fukuda, Tomohiro and Yabuki, Nobuyoshi |
| year |
2019 |
| title |
Quantifying Visual Environment by Semantic Segmentation Using Deep Learning - A Prototype for Sky View Factor |
| source |
M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 623-632 |
| doi |
https://doi.org/10.52842/conf.caadria.2019.2.623
|
| summary |
Sky view factor (SVF) is the ratio of radiation received by a planar surface from the sky to that received from the entire hemispheric radiating environment, in the past 20 years, it was more applied to urban-climatic areas such as urban air temperature analysis. With the urbanization and the development of cities, SVF has been paid more and more attention on as the important parameter in urban construction and city planning area because of increasing building coverage ratio to promote urban forms and help creating a more comfortable and sustainable urban residential building environment to citizens. Therefore, efficient, low cost, high precision, easy to operate, rapid building-wide SVF estimation method is necessary. In the field of image processing, semantic segmentation based on deep learning have attracted considerable research attention. This study presents a new method to estimate the SVF of residential environment by constructing a deep learning network for segmenting the sky areas from 360-degree camera images. As the result of this research, an easy-to-operate estimation system for SVF based on high efficiency sky label mask images database was developed. |
| keywords |
Visual environment; Sky view factor; Semantic segmentation; Deep learning; Landscape simulation |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (9,494,166 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:54 |
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