A machine learning-based method for the large-scale evaluation of the qualities of the urban environment

Liu, Lun; Silva, Elisabete; Wu, Chunyang; Wang, Hui

doi:10.1016/j.compenvurbsys.2017.06.003

Cited by 227 publications

(130 citation statements)

References 25 publications

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“…Similarly, various models have been developed to classify weather from features extracted based on a convolution structure of deep models. For instance, a CNN model coupled with sparse decomposition was trained to classify weather conditions [25]. Additionally, a binary CNN model was trained to classify images as either cloudy or sunny [26,27].…”

Section: Deep Learning Modelsmentioning

confidence: 99%

WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning

Ibrahim

Haworth

Cheng

2019

IJGI

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Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it has still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used in practice. What has been achieved to-date are rather sectorial models that address a limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep convolutional neural network (CNN) models, so-called WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as dawn/dusk, day and night for time detection, glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used but are not limited to autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers. environment due to precipitation including clear, rainy, foggy, or snowy weather. They represent crucial factors for many urban studies including transport, behaviour, and safety-related research [5]. For example, walking, cycling, or driving in rainy weather is associated with a higher risk of experiencing an incident than in clear weather [5,6]. Fog, snow, and glare have also been found to increase risk [6,7]. Importantly, it is not only the inherent risk that different weather and visual conditions pose to human life that is of interest to researchers. Scene awareness for autonomous navigation in cities is highly influenced by the dynamics of weather and visual conditions and it is imperative for any vision system to cope with them simultaneously [8]. For example, object detection algorithms must perform well in fog and glare as well as in clear conditions, in order to be reliable. Accordingly, finding an automatic approach to extract this information from images or video streams is in high demand for computer scientists, planners, and policy-makers.While there are different methods that are used to understand the dynamics of weather and visual conditions, a knowledge gap appears when addressing this subject. To date, these two crucial domains-weather and visual conditions-have been studied individually, ignoring the importance of understanding the dy...

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Section: Deep Learning Modelsmentioning

confidence: 99%

WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning

Ibrahim

Haworth

Cheng

2019

IJGI

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“…Despite the importance of active frontages in the urban design literature and the ubiquity of urban image data, limited urban computational research has been conducted on the classification of street frontages using street image data (Law et al 2017;Liu et al 2017). Previous computational research on street image data involved manual feature extraction to detect edges for classification.…”

Section: Related Workmentioning

confidence: 99%

“…More recently, these techniques have been used to estimate greenery and street enclosures, which were found to have an effect on walkability (Li et al 2018). More closely related, CNNs have also been used to predict the continuity of a street facade as rated by domain-experts (Liu et al 2017) and in developing the Street Frontage Net model (SFN), a street frontage classifier, using both Google Street View images and 3D-model synthetic street images (Law et al 2017).…”

Section: Related Workmentioning

confidence: 99%

Street-Frontage-Net: urban image classification using deep convolutional neural networks

Law

Seresinhe

Shen

et al. 2018

International Journal of Geographical Information Science

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Street-Frontage-Net: urban image classification using deep convolutional neural networksQuantifying aspects of urban design on a massive scale is crucial to help develop a deeper understanding of urban designs elements that contribute to the success of a public space. In this study, we further develop the Street-Frontage-Net (SFN), a convolutional neural network (CNN) that can successfully evaluate the quality of street frontage as either being active (frontage containing windows and doors) or blank (frontage containing walls, fences and garages). Small scale studies have indicated that the more active the frontage, the livelier and safer a street feels.However, collecting the city-level data necessary to evaluate street frontage quality is costly. The SFN model uses a deep CNN to classify the frontage of a street. This study expands on the previous research via five experiments. We find robust results in classifying frontage quality for an out-of-sample test set that achieves an accuracy of up to 92.0%. We also find active frontages in a neighbourhood has a significant link with increased house prices. Lastly, we find that active frontage is associated with more scenicness compared to blank frontage. While further research is needed, the results indicate the great potential for using deep learning methods in geographic information extraction and urban design.

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“…The study of the impact of UGS on public health [3][4][5][6][7], to manage the urban ecosystem [8,9] or to assess the aesthetic quality of UGS [10] can benefit from various computer vision based approaches. This includes computer vision to acquire the semantic information of every single pixel of an urban space [11][12][13][14][15] or analyzing the visual impact of vegetation in urban environments [16][17][18] from top view images in birds or satellite viewpoint.…”

Section: Introductionmentioning

confidence: 99%

Toward a Computer Vision Perspective on the Visual Impact of Vegetation in Symmetries of Urban Environments

et al. 2018

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Rapid urbanization is a worldwide critical environmental challenge. With this urban migration soaring, we need to live far more efficiently than we currently do by incorporating the natural world in new and innovative ways. There are a lot of researches on ecological, architectural or aesthetic points of view to address this issue. We present a novel approach to assess the visual impact of vegetation in urban street pedestrian view with the assistance of computer vision metrics. We statistically evaluate the correlations of the amount of vegetation with objective computer vision traits such as Fourier domain, color histogram, and estimated depth from monocular view. We show that increasing vegetation in urban street views breaks the orthogonal symmetries of urban blocks, enriches the color space with fractal-like symmetries and decreases the cues of projective geometry in depth. These uncovered statistical facts are applied to predict the requested amount of vegetation to make urban street views appear like natural images. Interestingly, these amounts are found in accordance with the ecosystemic approach for urban planning. Also, the study opens new questions for the understanding of the link between geometry and depth perception.

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A machine learning-based method for the large-scale evaluation of the qualities of the urban environment

Cited by 227 publications

References 25 publications

WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning

WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning

Street-Frontage-Net: urban image classification using deep convolutional neural networks

Toward a Computer Vision Perspective on the Visual Impact of Vegetation in Symmetries of Urban Environments

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