000			02785nab a2200289 4500
999			_c11665 _d11665
003			OSt
005			20210517124603.0
007			cr aa aaaaa
008			210517b ||||| |||| 00| 0 eng d
100			_aYe, Yu _945648
245			_aVisual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images
260			_bSage, _c2019.
300			_aVol 46, Issue 8, 2019 ( 1439-1457 p.)
520			_aThis study proposes a workable approach for quantitatively measuring the perceptual-based visual quality of streets, which has often relied on subjective impressions or feelings. With the help of recently emerged street view images and machine learning algorithms, an evaluation model has been trained to assess the perceived visual quality with accuracy similar to that of experienced urban designers, to provide full coverage and detailed results for a citywide area. The town centre of Shanghai was selected for the site. Around 140,000 screenshots from Baidu Street View were processed and a machine learning algorithm, SegNet, was applied to intelligently extract the pixels representing key elements affecting the visual quality of streets, including the building frontage, greenery, sky view, pedestrian space, motorisation, and diversity. A Java-based program was then produced to automatically collect the preferences of experienced urban designers on representative sample images. Another machine learning algorithm, i.e. an artificial neural network, was used to train an evaluation model to achieve a citywide, high-resolution evaluation of the visual quality of the streets. Further validation through different approaches shows this evaluation model obtains a satisfactory accuracy. The results from the artificial neural network also help to explore the high or low effects of various key elements on visual quality. In short, this study contributes to the development of human-centred planning and design by providing continuous measurements of an ‘unmeasurable’ quality across large-scale areas. Meanwhile, insights on the perceptual-based visual quality and detailed mapping of various key elements in streets can assist in more efficient street renewal by providing accurate design guidance.
650			_aStreet view images, _946032
650			_a machine learning, _946033
650			_aurban design, _946034
650			_astreet, _946035
650			_avisual quality _937441
700			_a Zeng, Wei _946036
700			_aShen, Qiaomu _934606
700			_aZhang, Xiaohu _946037
700			_aLu, Yi _931464
773	0		_011590 _915512 _dSage 2019. _t Environment and Planning B: Urban Analytics and City Science
856			_uhttps://doi.org/10.1177/2399808319828734
942			_2ddc _cART