Urban environments offer a wealth of opportunities for residents to respite from their hectic life.Outdoor running or jogging becomes increasingly popular of an option.Impacts of urban environments on outdoor running,...Urban environments offer a wealth of opportunities for residents to respite from their hectic life.Outdoor running or jogging becomes increasingly popular of an option.Impacts of urban environments on outdoor running,despite some initial studies,remain underexplored.This study aims to establish an analytical framework that can holistically assess the urban environment on the healthy vitality of running.The proposed framework is applied to two modern Chinese cities,i.e.,Guangzhou and Shenzhen.We construct three interpretable random forest models to explore the non-linear relationship between environmental variables and running intensity(RI)through analyzing the runners'trajectories and integrating with multi-source urban big data(e.g.,street view imagery,remote sensing,and socio-economic data)across the built,natural,and social dimensions,The findings uncover that road density has the greatest impact on RI,and social variables(e.g.,population density and housing price)and natural variables(e.g.,slope and humidity)all make notable impact on outdoor running.Despite these findings,the impact of environmental variables likely change across different regions due to disparate regional construction and micro-environments,and those specific impacts as well as optimal thresholds also alter.Therefore,construction of healthy cities should take the whole urban environment into account and adapt to local conditions.This study provides a comprehensive evaluation on the influencing variables of healthy vitality and guides sustainable urban planning for creating running-friendly cities.展开更多
Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems.These methods have been very reliable in measuring vegetation coverage accurate...Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems.These methods have been very reliable in measuring vegetation coverage accurately at the top of the canopy,but their capabilities are limited when it comes to identifying green vegetation located beneath the canopy cover.Measuring the amount of urban and suburban vegetation along a street network that is partially beneath the canopy has recently been introduced with the use of Google Street View(GSV)images,made accessible by the Google Street View Image API.Analyzing green vegetation through the use of GSV images can provide a comprehensive representation of the amount of green vegetation found within geographical regions of higher population density,and it facilitates an analysis performed at the street-level.In this paper we propose a fine-tuned color based image filtering and segmentation technique and we use it to define and map an urban green environment index.We deployed this image processing method and,using GSV images as a high-resolution GIS data source,we computed and mapped the green index of Milwaukee County,a 3,082 km^(2) urban/suburban county in Wisconsin.This approach generates a high-resolution street-level vegetation estimate that may prove valuable in urban planning and management,as well as for researchers investigating the correlation between environmental factors and human health outcomes.展开更多
Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt ver...Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.展开更多
The urban environment affects human behavior and health.Most studies on the feelings of street spaces have not considered a specific kind of realistic scene,such as running.To overcome this limitation,we explored the ...The urban environment affects human behavior and health.Most studies on the feelings of street spaces have not considered a specific kind of realistic scene,such as running.To overcome this limitation,we explored the relationship between the urban environment and the pleasure of running.We collected 8260 street view images from 153 running routes in Beijing and invited more than 400 volunteers of different genders and ages to rate their sense of pleasure in street view images of the urban running environment through an online survey.Then,the proportion of visual elements in street images was extracted based on semantic segmentation,and the landscape was divided.Finally,a linear mixed model was used to predict the pleasure scores of different gender and age groups for different landscapes.The results show significant differences in the pleasure scores for different landscapes and age groups.Middle-aged people’s sense of pleasure was lower than that of the young and the elderly.More greenery was associated with a higher pleasure score,while the proportion of urban elements such as buildings was negatively correlated with the pleasure score.The results indicate that running in a natural landscape is pleasurable and beneficial for mental health.展开更多
With the continuous calls for energy conservation and emission reduction in recent years,more and more people choose walking as their travel mode.The improvement of the quality of street space will directly affect peo...With the continuous calls for energy conservation and emission reduction in recent years,more and more people choose walking as their travel mode.The improvement of the quality of street space will directly affect people's willingness to walk.By sorting out relevant research on street quality measurement,extracting quality keywords with high frequency of reference as impact factors,and using street view image data from different eras,semantic segmentation technology,factor analysis,and questionnaire survey methods,this paper evaluates the street quality of Jingshan East Street,Dongcheng District,Beijing,further explores the impact of different factors on street quality,and analyzes possible ways to improve it.展开更多
Urban visual environment plays a critical role in city planning and public health by influencing residents’emotional responses.However,existing studies rely on subjective assessments of small-scale areas,lacking obje...Urban visual environment plays a critical role in city planning and public health by influencing residents’emotional responses.However,existing studies rely on subjective assessments of small-scale areas,lacking objective,large-scale emotional maps.This study introduces a novel electroencephalogram(EEG)-based framework to quantify emotional responses to urban visual environments.First,urban visual indicators are extracted from 352,112 street view images(SVI)in Guangzhou and 680,280 in Shenzhen using a deep learning-based semantic segmentation.Then,EEG experiments are conducted with 24 participants exposed to four groups of SVI visual stimuli,employing machine learning models to quantify relationships between visual indicators and emotions.Finally,a series of models are integrated to generate city-wide visual emotional maps across four emotion dimensions and valence-arousal space.Results show that the models established between the visual environment and emotional responses display R2 values in the range of 0.39 to 0.69,enabling visual emotional mapping.Correlation analysis further shows a higher green view index and color entropy significantly correlate with positive emotions(p<0.01).Conversely,elevated building density and openness are linked to negative emotions(p<0.01).In terms of HAPV(high-arousal,positive-valence)dimensions,Shenzhen had higher emotion scores than Guangzhou,with mean values of 0.237 and 0.226,respectively.These differences correspond to colorful urban center landscapes in Shenzhen and predominantly green landscapes in Guangzhou as a result of their different urban planning strategy.This study contributes to the optimization and transformation of urban landscapes for improved visual comfort.展开更多
As simulation-informed design gains importance in addressing urban complexity,integrating urban imagery into interactive feedback and decision-making has become increasingly essential.However,this potential remains un...As simulation-informed design gains importance in addressing urban complexity,integrating urban imagery into interactive feedback and decision-making has become increasingly essential.However,this potential remains underused,as urban imagery is often treated as a supporting variable in urban research rather than a core layer of spatial intelligence,hindering informed strategies in city branding,resource allocation,and livability.This study develops a data-driven framework,Street View Search Engine,which integrates urban imagery analysis with interactive exploration to advance human-centered insights into urban visual form.Based on 81,478 street view imagery collected in Hong Kong,China,a dataset comprising 19 visual features was first constructed to represent urban visual information across three categories:physical,impression,and isovist.Subsequently,the machine learning algorithm self-organizing maps was employed to train the dataset,producing a visualized“data landscape”that re-organizes street views according to their visual similarities.Third,building on the data landscape,this study develops the Street View Search Engine framework to conduct three main tasks:define visual foundations,comprehend streetscape morphology,and evaluate regional visual schemes.These tasks combine general-use exploration with research-oriented analysis:a web-based platform was developed to support general-use exploration(http://47.113.226.77/project1/#/),while various data processing methods were employed to enable in-depth professional investigations.By transforming raw data into a visualizable,computable,and interactive urban imagery system,this study paves the way for evidence-based interventions,strategic resource allocation,and greater public engagement in urban planning.展开更多
Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch...Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch images from Google Street View(GSV);(2)label images with a custom Rhino-based tool to aid annotation of occluded glazing;(3)detect wall,garage,and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts;(4)calculate WWR at façade,building,and district scales;and(5)simulate district energy consumption in an urban building energy model(UBEM).Results include a 96%image-capture rate from GSV,indicating a robust extraction and stitching algorithm.Converting model detections into WWR,94%and 100%of façades have detected WWRs within±5%and±10%of ground truth WWRs,respectively.A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears,resulting in distinct WWRs for each face of each building.For a case study in Turin,Italy,WWR detections are+5.2%and+6.9%greater when upscaling based on OpenStreetMap and municipal GIS data,respectively,compared to TABULA,leading to 1.5%and 35.5%increases in heating and cooling energy need in the UBEM.The workflow is made openly available to support future research in other contexts.展开更多
This paper considers panorama images used for street views. Their viewing angle of 360° causes pixels at the top and bottom to appear stretched and warped. Although current image completion algorithms work well, ...This paper considers panorama images used for street views. Their viewing angle of 360° causes pixels at the top and bottom to appear stretched and warped. Although current image completion algorithms work well, they cannot be directly used in the presence of such distortions found in panoramas of street views. We thus propose a novel approach to complete such 360° panoramas using optimizationbased projection to deal with distortions. Experimental results show that our approach is efficient and provides an improvement over standard image completion algorithms.展开更多
Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management.Recently,the development of deep learning technology and big data of street view i...Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management.Recently,the development of deep learning technology and big data of street view images,makes it possible to quantitatively explore the relationship between streetscape and crime.This study computed eight streetscape indexes of the street built environment using Google Street View images firstly.Then,the association between the eight indexes and recorded crime events was revealed with a poisson regression model and a geographically weighted poisson regression model.An experiment was conducted in downtown and uptown Manhattan,New York.Global regression results show that the influences of Motorization Index on crimes are significant and positive,while the effects of the Light View Index and Green View Index on crimes depend heavily on the socioeconomic factors.From a local perspective,the Pedestrian Space Index,Green View Index,Light View Index and Motorization Index have a significant spatial influence on crimes,while the same visual streetscape factors have different effects on different streets due to the combination differences of socioeconomic,cultural and streetscape elements.The key streetscape elements of a given street that affect a specific criminal activity can be identified according to the strength of the association.The results provide both theoretical and practical implications for crime theories and crime prevention efforts.展开更多
Numerous news or event pictures are taken and shared on the internet every day that have abundant information worth being mined,but only a small fraction of them are geotagged.The visual content of the news image hint...Numerous news or event pictures are taken and shared on the internet every day that have abundant information worth being mined,but only a small fraction of them are geotagged.The visual content of the news image hints at clues of the geographical location because they are usually taken at the site of the incident,which provides a prerequisite for geo-localization.This paper proposes an automated pipeline based on deep learning for the geo-localization of news pictures in a large-scale urban environment using geotagged street view images as a reference dataset.The approach obtains location information by constructing an attention-based feature extraction network.Then,the image features are aggregated,and the candidate street view image results are retrieved by the selective matching kernel function.Finally,the coordinates of the news images are estimated by the kernel density prediction method.The pipeline is tested in the news pictures in Hong Kong.In the comparison experiments,the proposed pipeline shows stable performance and generalizability in the large-scale urban environment.In addition,the performance analysis of components in the pipeline shows the ability to recognize localization features of partial areas in pictures and the effectiveness of the proposed solution in news picture geo-localization.展开更多
While new buildings continue to emerge in the process of urbanization,historical buildings,as valuable legacies carrying national historical memory,play an important role in the urban landscape.Previous studies have s...While new buildings continue to emerge in the process of urbanization,historical buildings,as valuable legacies carrying national historical memory,play an important role in the urban landscape.Previous studies have shown that color harmony is a crucial factor in coordinating urban landscapes.However,the evaluation of color harmony in historic areas and buildings lacks effective quantitative standards,often overlooking factors such as complementary color harmony and the compatibility of analogous colors.This study aims to build a new method to evaluate the color harmony of historical buildings through street view technology,semantic segmentation algorithms,quantification of color harmony methods based on image property detection and classification,questionnaire verification,and takes Shanghai’s historical buildings as an example to explore.Our study categorizes six types of color harmony indexes for Shanghai street-facing historic buildings into three levels,with the top tier serving as a benchmark for excellence and the lowest tier highlighting areas in need of urban environmental improvement.This study uniquely considers color compatibility within hue ranges and expanded relationship types like complementary harmony.This approach,applicable to cities globally,offers practical tools for urban planners and conservators in managing and preserving historic areas and buildings.展开更多
There is increasing interest in using Google Street View(GSV) for research purposes, particularly with regard to ‘‘virtually auditing'' the built environment to assess environmental quality. Research in this...There is increasing interest in using Google Street View(GSV) for research purposes, particularly with regard to ‘‘virtually auditing'' the built environment to assess environmental quality. Research in this field to date generally suggests GSV is a reliable means of understanding the ‘‘real world'' environment. But limitations around the dates and resolution of images have been identified. An emerging strand within this literature is also concerned with the potential of GSV to understand recovery post-disaster. Using the GSV data set for the evacuated area around the Fukushima Dai'ichi nuclear power plant as a case study, this article evaluates GSV as a means of assessing disaster recovery in a dynamic situation with remaining uncertainty and a significant value and emotive dimension. The article suggests that GSV does have value in giving a high-level overview of the postdisaster situation and has potential to track recovery and resettlement over time. Drawing on social science literature relating to Fukushima, and disasters more widely, the article also argues it is imperative for researchers using GSV to reflect carefully on the wider socio-cultural contexts that are often not represented in the photo montage.展开更多
Regular evaluation of street space is essential for achieving sustainable development and dynamic maintenance of historic areas.Although quantitative evaluations using street view imagery are precise and efficient,the...Regular evaluation of street space is essential for achieving sustainable development and dynamic maintenance of historic areas.Although quantitative evaluations using street view imagery are precise and efficient,they often fall short in capturing pedestrians’visual experience,largely because images are collected from vehicles.Accordingly,this paper acquires street view imagery in the human visual field before and after the street space renovation by adjusting relevant parameters,and performs image semantic segmentation.From a pedestrian’s viewpoint,the paper develops street space evaluation indicators across four dimensions:comfort,identity,diversity,and walkability.The mean square deviation method is applied to assign weights to these indicators,enabling a comprehensive evaluation of street space in historic areas.In addition to evaluating the renovation results,it proposes improvement suggestions that may provide insights into the evaluation practices of street space renovations in historic areas and contribute to improving street space quality.展开更多
基金National Natural Science Foundation of China,No.42171455The Hong Kong RGC Research Impact Fund,No.R5011-23The Hong Kong General Research Fund,No.15204121。
文摘Urban environments offer a wealth of opportunities for residents to respite from their hectic life.Outdoor running or jogging becomes increasingly popular of an option.Impacts of urban environments on outdoor running,despite some initial studies,remain underexplored.This study aims to establish an analytical framework that can holistically assess the urban environment on the healthy vitality of running.The proposed framework is applied to two modern Chinese cities,i.e.,Guangzhou and Shenzhen.We construct three interpretable random forest models to explore the non-linear relationship between environmental variables and running intensity(RI)through analyzing the runners'trajectories and integrating with multi-source urban big data(e.g.,street view imagery,remote sensing,and socio-economic data)across the built,natural,and social dimensions,The findings uncover that road density has the greatest impact on RI,and social variables(e.g.,population density and housing price)and natural variables(e.g.,slope and humidity)all make notable impact on outdoor running.Despite these findings,the impact of environmental variables likely change across different regions due to disparate regional construction and micro-environments,and those specific impacts as well as optimal thresholds also alter.Therefore,construction of healthy cities should take the whole urban environment into account and adapt to local conditions.This study provides a comprehensive evaluation on the influencing variables of healthy vitality and guides sustainable urban planning for creating running-friendly cities.
基金This work was supported by the National Science Foundation [DUE-1129056]This research was completed under the University of Wisconsin-Milwaukee’s Undergraduate Research in Biology and Mathematics(UBM)Program and was supported by a grant from the National Science Foundation DUE-1129056.Additional support was provided from the University of Wisconsin-Milwaukee’s Support For Undergraduate Research Fellowship(SURF),issued by UW-Milwaukee’s Office of Undergraduate Research.The authors of this paper would like to thank Prof.Gabriella Pinter,Prof.Erica Young and Prof.John Berges for their invaluable support.Finally,the authors would like recognize Google LLC for its publicly available image resource and street view API,without which this investigation would not have been possible.
文摘Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems.These methods have been very reliable in measuring vegetation coverage accurately at the top of the canopy,but their capabilities are limited when it comes to identifying green vegetation located beneath the canopy cover.Measuring the amount of urban and suburban vegetation along a street network that is partially beneath the canopy has recently been introduced with the use of Google Street View(GSV)images,made accessible by the Google Street View Image API.Analyzing green vegetation through the use of GSV images can provide a comprehensive representation of the amount of green vegetation found within geographical regions of higher population density,and it facilitates an analysis performed at the street-level.In this paper we propose a fine-tuned color based image filtering and segmentation technique and we use it to define and map an urban green environment index.We deployed this image processing method and,using GSV images as a high-resolution GIS data source,we computed and mapped the green index of Milwaukee County,a 3,082 km^(2) urban/suburban county in Wisconsin.This approach generates a high-resolution street-level vegetation estimate that may prove valuable in urban planning and management,as well as for researchers investigating the correlation between environmental factors and human health outcomes.
基金supported by the US National Science Foundation under Grant No. 1612843. NHERI Design Safe (Rathje et al., 2017)Texas Advanced Computing Center (TACC)。
文摘Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.
基金China’s National Key Research and Development Program,No.2017YFB0503500National Natural Science Foundation of China,No.41901321。
文摘The urban environment affects human behavior and health.Most studies on the feelings of street spaces have not considered a specific kind of realistic scene,such as running.To overcome this limitation,we explored the relationship between the urban environment and the pleasure of running.We collected 8260 street view images from 153 running routes in Beijing and invited more than 400 volunteers of different genders and ages to rate their sense of pleasure in street view images of the urban running environment through an online survey.Then,the proportion of visual elements in street images was extracted based on semantic segmentation,and the landscape was divided.Finally,a linear mixed model was used to predict the pleasure scores of different gender and age groups for different landscapes.The results show significant differences in the pleasure scores for different landscapes and age groups.Middle-aged people’s sense of pleasure was lower than that of the young and the elderly.More greenery was associated with a higher pleasure score,while the proportion of urban elements such as buildings was negatively correlated with the pleasure score.The results indicate that running in a natural landscape is pleasurable and beneficial for mental health.
文摘With the continuous calls for energy conservation and emission reduction in recent years,more and more people choose walking as their travel mode.The improvement of the quality of street space will directly affect people's willingness to walk.By sorting out relevant research on street quality measurement,extracting quality keywords with high frequency of reference as impact factors,and using street view image data from different eras,semantic segmentation technology,factor analysis,and questionnaire survey methods,this paper evaluates the street quality of Jingshan East Street,Dongcheng District,Beijing,further explores the impact of different factors on street quality,and analyzes possible ways to improve it.
基金supported by the National Natural Science Foundation of China(No.42475075,U2442212)Guangdong Basic and Applied Basic Research Foundation(No.2025A1515011854)Guangzhou Science and Technology Plan Project(No.2025A04J5204).
文摘Urban visual environment plays a critical role in city planning and public health by influencing residents’emotional responses.However,existing studies rely on subjective assessments of small-scale areas,lacking objective,large-scale emotional maps.This study introduces a novel electroencephalogram(EEG)-based framework to quantify emotional responses to urban visual environments.First,urban visual indicators are extracted from 352,112 street view images(SVI)in Guangzhou and 680,280 in Shenzhen using a deep learning-based semantic segmentation.Then,EEG experiments are conducted with 24 participants exposed to four groups of SVI visual stimuli,employing machine learning models to quantify relationships between visual indicators and emotions.Finally,a series of models are integrated to generate city-wide visual emotional maps across four emotion dimensions and valence-arousal space.Results show that the models established between the visual environment and emotional responses display R2 values in the range of 0.39 to 0.69,enabling visual emotional mapping.Correlation analysis further shows a higher green view index and color entropy significantly correlate with positive emotions(p<0.01).Conversely,elevated building density and openness are linked to negative emotions(p<0.01).In terms of HAPV(high-arousal,positive-valence)dimensions,Shenzhen had higher emotion scores than Guangzhou,with mean values of 0.237 and 0.226,respectively.These differences correspond to colorful urban center landscapes in Shenzhen and predominantly green landscapes in Guangzhou as a result of their different urban planning strategy.This study contributes to the optimization and transformation of urban landscapes for improved visual comfort.
基金supported by the National Natural Science Foundation of China(Grant No.52308015)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2024A1515011214)the Guangzhou Science and Technology Planning Project(Grant No.SL2024A04J01189).
文摘As simulation-informed design gains importance in addressing urban complexity,integrating urban imagery into interactive feedback and decision-making has become increasingly essential.However,this potential remains underused,as urban imagery is often treated as a supporting variable in urban research rather than a core layer of spatial intelligence,hindering informed strategies in city branding,resource allocation,and livability.This study develops a data-driven framework,Street View Search Engine,which integrates urban imagery analysis with interactive exploration to advance human-centered insights into urban visual form.Based on 81,478 street view imagery collected in Hong Kong,China,a dataset comprising 19 visual features was first constructed to represent urban visual information across three categories:physical,impression,and isovist.Subsequently,the machine learning algorithm self-organizing maps was employed to train the dataset,producing a visualized“data landscape”that re-organizes street views according to their visual similarities.Third,building on the data landscape,this study develops the Street View Search Engine framework to conduct three main tasks:define visual foundations,comprehend streetscape morphology,and evaluate regional visual schemes.These tasks combine general-use exploration with research-oriented analysis:a web-based platform was developed to support general-use exploration(http://47.113.226.77/project1/#/),while various data processing methods were employed to enable in-depth professional investigations.By transforming raw data into a visualizable,computable,and interactive urban imagery system,this study paves the way for evidence-based interventions,strategic resource allocation,and greater public engagement in urban planning.
文摘Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch images from Google Street View(GSV);(2)label images with a custom Rhino-based tool to aid annotation of occluded glazing;(3)detect wall,garage,and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts;(4)calculate WWR at façade,building,and district scales;and(5)simulate district energy consumption in an urban building energy model(UBEM).Results include a 96%image-capture rate from GSV,indicating a robust extraction and stitching algorithm.Converting model detections into WWR,94%and 100%of façades have detected WWRs within±5%and±10%of ground truth WWRs,respectively.A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears,resulting in distinct WWRs for each face of each building.For a case study in Turin,Italy,WWR detections are+5.2%and+6.9%greater when upscaling based on OpenStreetMap and municipal GIS data,respectively,compared to TABULA,leading to 1.5%and 35.5%increases in heating and cooling energy need in the UBEM.The workflow is made openly available to support future research in other contexts.
基金supported by the National Basic Research Program of China (No. 2011CB302205)the National Natural Science Foundation of China (No. 61120106007)+3 种基金the National High-tech R&D Program of China (No. 2012AA011802)EPSRC Travel Granta research grant of Beijing Higher Institution Engineering Research CenterTsinghua University Initiative Scientific Research Program
文摘This paper considers panorama images used for street views. Their viewing angle of 360° causes pixels at the top and bottom to appear stretched and warped. Although current image completion algorithms work well, they cannot be directly used in the presence of such distortions found in panoramas of street views. We thus propose a novel approach to complete such 360° panoramas using optimizationbased projection to deal with distortions. Experimental results show that our approach is efficient and provides an improvement over standard image completion algorithms.
基金supported by the National Natural Science Foundation of China(Grant No.61872050,No.62172066)the Chongqing Basic and Frontier Research Program(cste2018jcyjAX0551),the FundamentaRl esearchFundsforthe,Central Universityes(2018CDJSK03XK01)the Chongqing Technology Innovation and Application Development Key Project(ctsc2019jscx-gksbx0066)。
文摘Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management.Recently,the development of deep learning technology and big data of street view images,makes it possible to quantitatively explore the relationship between streetscape and crime.This study computed eight streetscape indexes of the street built environment using Google Street View images firstly.Then,the association between the eight indexes and recorded crime events was revealed with a poisson regression model and a geographically weighted poisson regression model.An experiment was conducted in downtown and uptown Manhattan,New York.Global regression results show that the influences of Motorization Index on crimes are significant and positive,while the effects of the Light View Index and Green View Index on crimes depend heavily on the socioeconomic factors.From a local perspective,the Pedestrian Space Index,Green View Index,Light View Index and Motorization Index have a significant spatial influence on crimes,while the same visual streetscape factors have different effects on different streets due to the combination differences of socioeconomic,cultural and streetscape elements.The key streetscape elements of a given street that affect a specific criminal activity can be identified according to the strength of the association.The results provide both theoretical and practical implications for crime theories and crime prevention efforts.
文摘Numerous news or event pictures are taken and shared on the internet every day that have abundant information worth being mined,but only a small fraction of them are geotagged.The visual content of the news image hints at clues of the geographical location because they are usually taken at the site of the incident,which provides a prerequisite for geo-localization.This paper proposes an automated pipeline based on deep learning for the geo-localization of news pictures in a large-scale urban environment using geotagged street view images as a reference dataset.The approach obtains location information by constructing an attention-based feature extraction network.Then,the image features are aggregated,and the candidate street view image results are retrieved by the selective matching kernel function.Finally,the coordinates of the news images are estimated by the kernel density prediction method.The pipeline is tested in the news pictures in Hong Kong.In the comparison experiments,the proposed pipeline shows stable performance and generalizability in the large-scale urban environment.In addition,the performance analysis of components in the pipeline shows the ability to recognize localization features of partial areas in pictures and the effectiveness of the proposed solution in news picture geo-localization.
基金funded by National Key R&D Program of China(2023YFC3805503).
文摘While new buildings continue to emerge in the process of urbanization,historical buildings,as valuable legacies carrying national historical memory,play an important role in the urban landscape.Previous studies have shown that color harmony is a crucial factor in coordinating urban landscapes.However,the evaluation of color harmony in historic areas and buildings lacks effective quantitative standards,often overlooking factors such as complementary color harmony and the compatibility of analogous colors.This study aims to build a new method to evaluate the color harmony of historical buildings through street view technology,semantic segmentation algorithms,quantification of color harmony methods based on image property detection and classification,questionnaire verification,and takes Shanghai’s historical buildings as an example to explore.Our study categorizes six types of color harmony indexes for Shanghai street-facing historic buildings into three levels,with the top tier serving as a benchmark for excellence and the lowest tier highlighting areas in need of urban environmental improvement.This study uniquely considers color compatibility within hue ranges and expanded relationship types like complementary harmony.This approach,applicable to cities globally,offers practical tools for urban planners and conservators in managing and preserving historic areas and buildings.
文摘There is increasing interest in using Google Street View(GSV) for research purposes, particularly with regard to ‘‘virtually auditing'' the built environment to assess environmental quality. Research in this field to date generally suggests GSV is a reliable means of understanding the ‘‘real world'' environment. But limitations around the dates and resolution of images have been identified. An emerging strand within this literature is also concerned with the potential of GSV to understand recovery post-disaster. Using the GSV data set for the evacuated area around the Fukushima Dai'ichi nuclear power plant as a case study, this article evaluates GSV as a means of assessing disaster recovery in a dynamic situation with remaining uncertainty and a significant value and emotive dimension. The article suggests that GSV does have value in giving a high-level overview of the postdisaster situation and has potential to track recovery and resettlement over time. Drawing on social science literature relating to Fukushima, and disasters more widely, the article also argues it is imperative for researchers using GSV to reflect carefully on the wider socio-cultural contexts that are often not represented in the photo montage.
文摘Regular evaluation of street space is essential for achieving sustainable development and dynamic maintenance of historic areas.Although quantitative evaluations using street view imagery are precise and efficient,they often fall short in capturing pedestrians’visual experience,largely because images are collected from vehicles.Accordingly,this paper acquires street view imagery in the human visual field before and after the street space renovation by adjusting relevant parameters,and performs image semantic segmentation.From a pedestrian’s viewpoint,the paper develops street space evaluation indicators across four dimensions:comfort,identity,diversity,and walkability.The mean square deviation method is applied to assign weights to these indicators,enabling a comprehensive evaluation of street space in historic areas.In addition to evaluating the renovation results,it proposes improvement suggestions that may provide insights into the evaluation practices of street space renovations in historic areas and contribute to improving street space quality.
