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Article
Development Studies
Nanxi Su et al.
Summary: This study investigates the relationship between urban design quality and crime density around subway stations, finding significant associations between them. Certain design elements are related to higher crime risk while others are related to lower crime risk.
HABITAT INTERNATIONAL
(2023)
Article
Computer Science, Artificial Intelligence
Anthony Vanky et al.
Summary: With increasing computational power and advancements in deep learning methods, computer vision technologies have become widespread in urban environments. Their applications in policing, traffic management, and documenting public spaces are becoming increasingly common. However, these applications often oversimplify urban experiences and lack context and specificity, hindering semantic knowledge and analysis.
Article
Ecology
Arianna Salazar-Miranda et al.
Summary: This study proposes a new framework to measure street activity in real-time using machine learning and computer vision. They conducted field research in Paris for five weeks and created activity maps showing the differences in supporting pedestrian activity among streets. The framework can be applied in other cities, providing urban researchers with a method to guide planning interventions, identify infrastructure deficiencies, and inform design policies that promote active streets.
LANDSCAPE AND URBAN PLANNING
(2023)
Editorial Material
Chemistry, Analytical
Jing Tian
Article
Mathematics, Interdisciplinary Applications
Esra Suel et al.
Summary: This study uses deep learning methods and street-level images to analyze the inequality in socioeconomic and environmental features across 12 cities. The results show that visual features of disadvantaged neighborhoods are more distinct and unique in each city compared to affluent neighborhoods. This suggests that differences between cities are also influenced by historical factors, policies, and local geography.
Article
Construction & Building Technology
Pengyuan Liu et al.
Summary: This article introduces a method using an artificial intelligence framework to predict pedestrian comfort on sidewalks, improving prediction accuracy by considering the interactions between pedestrians and their surrounding environment, as well as spatial and temporal variations.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Plant Sciences
Jingwen Yuan et al.
Summary: There is a growing focus on the landscape and environment of ocean cities, and computer vision design technologies have overcome the limitations of traditional design materials. The purpose of this paper is to study the design of marine urban botanical landscapes based on computer vision technology and multimodal interaction design theory, so as to meet people's needs for viewing, leisure, and entertainment.
Article
Public, Environmental & Occupational Health
Longhao Zhang et al.
Summary: This study investigated the changes in COVID-19 infection rates and the urban built environment in Manhattan, New York. The study used computer vision to analyze the microscopic and macroscopic factors of the urban built environment. It found correlations between the urban built environment and COVID-19 transmission, exploring the mechanism of its influence.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Psychology, Multidisciplinary
Benjamin Beltzung et al.
Frontiers in Psychology
(2023)
Article
Urban Studies
Shuting Chen et al.
Summary: Public open space (POS) is important for urban areas, but assessing them can be tedious. This research introduces a new approach of using Street View Imagery (SVI) and Computer Vision (CV) in conjunction with geospatial and remote sensing data to automate and extend POS assessment. Subjective and objective indicators are developed, and CV algorithms are used for visual feature retrieval. A case study in Hong Kong and Singapore shows that SVI can be used for POS assessment with high accuracy, reflecting different aspects compared to previous approaches.
Article
Information Science & Library Science
Tan Yigitcanlar et al.
Summary: Despite the exponential growth in the popularity of AI, there is limited knowledge on public perception of AI in the context of local government services. This study aims to provide empirical evidence and insights on this topic. Findings indicate that attitude influences ease of use and perceived usefulness of AI, with Australians having a more positive perception compared to Hong Kongers. The research informs AI policy and planning decisions for local government authorities.
GOVERNMENT INFORMATION QUARTERLY
(2023)
Article
Computer Science, Interdisciplinary Applications
Brookie Guzder-Williams et al.
Summary: This paper presents a method using machine learning to automate the production of land use maps from satellite imagery. The researchers have trained a novel neural network architecture to generate high-resolution land use maps for a global sample of cities. They are currently working on generating land use maps for over 4,000 cities and metropolitan areas worldwide with populations exceeding 100,000.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Francisco Garrido-Valenzuela et al.
Summary: A thorough understanding of how urban space characteristics affect people's density in urban spaces is crucial for informed urban policy making. Previous studies have mainly focused on how urban space characteristics impact the number of people visiting different areas, but they are often limited to specific regions and their generalizability is unclear. This study utilizes computer vision technology and street-level images from the Netherlands to investigate the relationship between urban space and population density. The findings suggest that smaller blocks are associated with higher population density, indicating that compact urban development may be an effective strategy.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2023)
Article
Regional & Urban Planning
Zhong-Ren Peng et al.
Summary: Artificial intelligence (AI) is rapidly transforming and reshaping urban planning, but there are still many unanswered questions regarding its potential impacts, related issues, and appropriate responses and plans. This paper addresses these concerns through a scoping literature review and proposes a typology of AI in urban planning, ranging from AI-assisted and AI-augmented planning to AI-automated and eventually AI-autonomized planning.
JOURNAL OF PLANNING EDUCATION AND RESEARCH
(2023)
Article
Automation & Control Systems
Chenbo Zhao et al.
Summary: This study developed a new approach for estimating and analyzing land prices through deep learning that considered the streetscape and human subjective perception factors. Using street view images as input and land prices as output, we combined semantic segmentation results and human subjective perception scores with the results of land price estimation. We quantitatively modeled the relationship between streetscape and human subjective perception and determined the importance of streetscape factors for land prices through gradient-weighted class activation mapping and L1-based sparse linear regression.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Environmental Studies
Yiming Liu et al.
Summary: Sustainable development policies and spatial planning are crucial for major cities worldwide. This study introduces the green view index (GVI), which measures urban space quality through the visual perception of pedestrians and calculates the proportion of vegetation in road landscapes. Unlike macro indicators derived from planning or remote sensing data, the GVI starts from the bottom-up perception of individual residents and is more relevant to their subjective demands.
Article
Ecology
Bon Woo Koo et al.
Summary: This study examines the impact of microscale streetscape factors on pedestrian accessibility by analyzing street view images in Atlanta, Georgia, USA. The results show that microscale factors have significant correlations with pedestrian accessibility, and factors such as safety, pleasurability, and a composite microscale index directly influence walking mode choice. Additionally, traffic safety, safety from crime, and the composite microscale index enhance the benefits of pedestrian accessibility.
LANDSCAPE AND URBAN PLANNING
(2023)
Article
Ecology
Xiucheng Liang et al.
Summary: This study introduces an embedding-driven clustering approach that integrates physical and perceptual attributes to understand the spatial structure and spatio-temporal evolution of the urban visual environment. The research provides a novel method to analyze the urban visual structure and its evolution, which is significant for future planning decision-making and urban landscape improvement.
LANDSCAPE AND URBAN PLANNING
(2023)
Review
Construction & Building Technology
Tim Heinrich Son et al.
Summary: Artificial intelligence has the potential to support smart and sustainable development in urban planning through advanced data and analytical methods.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Environmental Studies
Bon Woo Koo et al.
Summary: This paper explores the correlation between street-level factors and neighborhood-level factors in relation to walkability by using computer vision techniques to analyze street view images in Atlanta, Georgia. The results suggest that street-level factors can significantly impact walking mode choices and may serve as proxies for macroscale factors, providing a different perspective on pedestrian experiences.
ENVIRONMENT AND BEHAVIOR
(2022)
Article
Hospitality, Leisure, Sport & Tourism
Kun Zhang et al.
Summary: This study conducted an empirical investigation of differing perceptions of nine types of urban space and nine visual elements among tourists using a computer vision approach. The findings showed that tourists from different continents had diverse perceptions on various elements in urban spaces, reflecting their cultural differences. The study provided insights into the construction of tourist gaze theory and the perception of tourism symbols in urban spaces.
Article
Ecology
Andrea Ghermandi et al.
Summary: Big data from photo-sharing platforms provide unique opportunities for studying human-nature interactions and landscape planning. This study analyzes ~10,000 outdoor photographs from three social media platforms to assess the impact of different image recognition services on clustering of photograph characteristics and other factors. The findings demonstrate that the choice of image recognition service significantly affects the results, highlighting the need for careful consideration in selecting the most appropriate service for specific applications.
LANDSCAPE AND URBAN PLANNING
(2022)
Article
Environmental Sciences
Xiang Xu et al.
Summary: In this study, the complementarity and conflicts between subjectively and objectively measured street-level perceptions in explaining property value were investigated. The findings showed that subjective measures were more effective in describing human perceptions, while objective measures were better suited for perceptions with self-evident connotations.
Article
Construction & Building Technology
Fang Wang et al.
Summary: This study selected several cities in the Yellow River Basin and the Rhine River Basin as examples, using street view images and computer vision analysis methods to propose a framework for quantitative research on locality expression. The results show significant differences among cities in the two countries, with some similarities within the basin. The protection of historic buildings in German cities and less intervention in the water environment have reference value for the development of Chinese cities.
INDOOR AND BUILT ENVIRONMENT
(2022)
Article
Geography
Yunhao Li et al.
Summary: This study aims to improve the performance of human perception analysis in urban planning modeling by enhancing existing computer vision models, resulting in a more objective, stable, and accurate neural network model.
TRANSACTIONS IN GIS
(2022)
Article
Hospitality, Leisure, Sport & Tourism
Nicole D. Payntar
Summary: By integrating large-scale internet photo datasets and computer algorithms, this study identifies emerging archaeological heritage attractions accessible from five Peruvian cities.
JOURNAL OF HERITAGE TOURISM
(2022)
Article
Ecology
Emily J. Wilkins et al.
Summary: The use of internet and social media provides valuable data for inferring landscape preferences, compared to traditional survey methods. However, the content of social media images and preferences derived from them often differ significantly from on-site intercept survey results. Additionally, sharing or not sharing park visit photographs on social media does not affect landscape preferences. Therefore, a combination of diverse data sources and analytical methods is recommended.
LANDSCAPE AND URBAN PLANNING
(2022)
Article
Green & Sustainable Science & Technology
Jiyun Lee et al.
Summary: Pedestrian-friendly cities are a global trend, and this study identified the physical and visual characteristics of the street environment that affect pedestrian satisfaction. The study analyzed vast amounts of visual data using computer vision techniques and found that visual features have a greater impact on pedestrian satisfaction than physical features. To create a highly satisfying street, it is important to consider the perspective of pedestrians and provide wide sidewalks, fewer lanes, and proper arrangement of street furniture.
Article
Construction & Building Technology
Maryam Hosseini et al.
Summary: This paper proposes CitySurfaces, a framework based on computer vision techniques that utilizes street-level images for sidewalk material classification. The framework is trained on images from New York City and Boston, demonstrating high accuracy, and is evaluated on images from other cities, showing its applicability and scalability.
SUSTAINABLE CITIES AND SOCIETY
(2022)
Article
Ecology
Junjie Luo et al.
Summary: Traditional approaches for river landscape evaluation are time-consuming and subjective. This study proposes a novel workflow using UAV photography, computer vision, and virtual reality to automatically and efficiently analyze rivers and surrounding landscapes and establish the relationship with human perception.
LANDSCAPE AND URBAN PLANNING
(2022)
Article
Construction & Building Technology
Zheng Li et al.
Summary: An increasing number of cities are advocating people-oriented street tree planning, and our study is the first research that combines street tree and pedestrian volume to propose planning suggestions.
BUILDING AND ENVIRONMENT
(2022)
Article
Environmental Sciences
Yang Zhang et al.
Summary: This research examines the spatial associations between the distribution of catering businesses and the design and planning of urban spaces in London. The results indicate that conflict and landscape spaces have a significant positive influence on the distribution of catering businesses, while open space has a significant negative influence. This research offers a quantitative approach to urban design and planning that can promote access to food, increase food options, and encourage active lifestyles.
Review
Chemistry, Analytical
Andrew A. Gumbs et al.
Summary: This review focuses on the advances and limitations of computer vision in surgery and discusses how it can contribute to achieving more autonomous actions. It also highlights the use of non-visual data in aiding robotic autonomy and addresses the current crisis regarding autonomy in surgical procedures.
Article
Geography
Krzysztof Janowicz et al.
Summary: This conversation discusses the development of artificial intelligence and machine learning in geography, with a focus on the legacy of the critical GIS movement and issues of data representation, bias, and blackboxing algorithms. The participants stress the need for accountability and the importance of critique.
DIALOGUES IN HUMAN GEOGRAPHY
(2022)
Article
Environmental Studies
Qiwei Song et al.
Summary: This study investigates the significance of subjective street perceptions and objective features on housing prices using street view imagery data in Shanghai. The findings reveal that both subjective perceptions and objective features significantly contribute to housing prices, with subjective perceptions showing more explanatory power and objective features exhibiting more collective strengths, suggesting that these two measures can complement each other.
Article
Construction & Building Technology
Tong Niu et al.
Summary: Small public spaces are crucial for citizens' living and socializing. This study proposes a systematic framework for quantifying vitality in small public spaces using fine-grained human trajectory data extracted from videos. A multi-indicator quantification method is utilized to comprehensively represent human vitality, resulting in a more precise evaluation model.
BUILDING AND ENVIRONMENT
(2022)
Article
Plant Sciences
Yanan Wang et al.
Summary: This study developed a workflow to explore design-related indicators that affect cultural ecosystem services (CES) in urban parks. Based on a case study in six urban parks in Beijing, three CES types were identified. Statistical analyses showed that the combination of four aspects of urban park features within a specific service radius was the most significant factor in explaining CES. Density of facilities, proportion of tree canopy-shaded ground, and richness of land cover types were found to be important for enhancing different CES types and could be considered in urban park design practices. The study also proposed landscape site design strategies and adaptive design cases to enhance CES types, providing evidence-based and practical solutions for managers and landscape architects.
URBAN FORESTRY & URBAN GREENING
(2022)
Article
Plant Sciences
Ye Zhang et al.
Summary: This study used computer vision tools to examine the effects of the physical environment of greenways on recreational activities in Singapore. The results showed a clustering pattern of different recreational activities at different time periods, which were found to be influenced by specific environmental features.
URBAN FORESTRY & URBAN GREENING
(2022)
Article
Multidisciplinary Sciences
Dany Doiron et al.
Summary: New 'big data' streams, such as street-level imagery, offer unprecedented possibilities for developing health-relevant data on the urban environment. This study used Google Street View images and computer vision methods to extract urban features and found that these features are better able to predict the percentage of people walking to work compared to traditional walkability metrics.
SCIENTIFIC REPORTS
(2022)
Article
Environmental Sciences
Xiaohe Yue et al.
Summary: This study utilizes Google Street View images to investigate the influence of built environments on chronic diseases and health behaviors in the United States. The findings suggest that walkability and urbanicity indicators are associated with better health outcomes, while single-lane roads and chain link fences are associated with poorer health.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Green & Sustainable Science & Technology
Shereen Wael et al.
Summary: This study examines the factors affecting user experiences at a Cairo Metro station in Egypt and finds that pedestrian flow, thermal comfort, safety levels, and destination proximity contribute to the user experience. The urban configuration with multiple elements also strongly affects metro user experience. This research provides valuable insights for guiding city planning and design in improving the user experience at metro stations.
Article
Ecology
Songyao Huai et al.
Summary: Urban parks are crucial for city dwellers to interact with nature. This study utilizes social media data and computer vision methods to assess the spatial and landscape preferences of tourists and locals in Brussels' urban parks. The findings highlight the differences in preferences between tourists and locals, providing insights for urban park planning and tourism management.
ECOSYSTEM SERVICES
(2022)
Article
Computer Science, Information Systems
Chenghao Yang et al.
Summary: Understanding public perceptions of urban public spaces through deep learning analysis of social media data provides new opportunities for improving urban vitality and spatial diversity. This study developed a VGG-16 image classification method and found that images of the Haihe River in Tianjin are dominated by skyscrapers, bridges, and promenades. The spatial distribution of Flickr images revealed three vitality areas in the river.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2022)
Review
Urban Studies
Jing Wang et al.
Summary: This paper provides a systematic review of the application of unsupervised learning in urban studies, revealing its methods, application areas, and trends. Unsupervised learning plays an important role in urban research, enabling the discovery of patterns and decision-making from complex data.
Article
Computer Science, Interdisciplinary Applications
Zhou Fang et al.
Summary: Limited attention has been given to human-computer interactions in the plan-making process. This paper proposes a methodological framework for interactive street network design that complements user-driven and example-driven approaches in urban planning and design. The proposed framework achieves better predictive performance compared to benchmark models, especially when limited planning guidance is available.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Zhanjun He et al.
Summary: This study proposes a multiscale analysis method to quantitatively study the influence of street built environment on crime occurrence using street-view images. The experimental results indicate that the proposed method accurately reveals the association between environmental features and crime occurrence.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2022)
Article
Biodiversity Conservation
Junjie Luo et al.
Summary: This study utilizes computer vision to analyze water view imagery and virtual reality to measure people's perceptions of water scenes. The results establish the relationship between subjective human perceptions and objective machine perceptions. The large-scale dataset produced in this study has been openly released to support future research.
ECOLOGICAL INDICATORS
(2022)
Review
Computer Science, Information Systems
Supriya Mahadevkar et al.
Summary: Computer applications have shifted from single data processing to machine learning due to the accessibility and availability of massive volumes of data obtained through the internet and various sources. This paper discusses the different machine learning techniques used in computer vision, deep learning, neural networks, and machine learning. The applications of machine learning in computer vision include object identification, object classification, and extracting usable information from images, graphic documents, and videos.
Article
Public, Environmental & Occupational Health
Quynh C. Nguyen et al.
Summary: This study utilized big data sources and computer vision technology to analyze the associations between built environment features and health outcomes in 2916 US counties. The findings showed that counties with more crosswalks were associated with lower adult obesity, physical inactivity, and poor self-rated health, highlighting the importance of pedestrian-friendly built environments in promoting better public health.
PUBLIC HEALTH REPORTS
(2021)
Article
Green & Sustainable Science & Technology
Md. Mustafizuri Rahman et al.
Summary: Urban planning is crucial for managing land use and describing built form, and reliable data and remote sensing technology can effectively support the achievement of Sustainable Development Goals. New methods and indices can help evaluate the implementation of land use plans and classify building types efficiently. The study shows that some commercial and industrial buildings do not comply with land use zoning, and some buildings have encroached upon conservation zones.
SUSTAINABILITY SCIENCE
(2021)
Article
Transportation Science & Technology
Koichi Ito et al.
Summary: The study evaluates urban bikeability comprehensively using street view imagery and computer vision, developing a comprehensive index composed of 34 indicators. The results show that street view imagery and computer vision are effective in assessing urban bikeability and have the potential to replace traditional techniques. However, combining street view imagery and non-street view imagery approaches may be the best way forward for future development.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Construction & Building Technology
Alessandro Massaro et al.
Summary: A new platform based on data processing is proposed for urban square design optimization. Experimental observations on public space influence, population density, and weather data are used for predictive analysis and correlation studies. The implemented Decision Support System provides guidelines for urban square design.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
David Koch et al.
Summary: This article examines the extent to which settlement patterns of university graduates can be derived from satellite images using a convolutional neural network (CNN). The study shows that there is information in satellite images that correlates with graduate density and that computer vision has high potential for urban and regional economics, particularly in data-poor regions. The approach utilized in this study provides a possible solution for the modifiable aerial unit (MAU) problem, which is a statistical bias that can distort spatial data analysis results.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Martin Knura et al.
Summary: Analyzing the demand for bicycle parking facilities in urban areas using object detection of social media images can provide valuable information for urban bicycle infrastructure planning, aiding in identifying potential locations for new bike parking facilities.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2021)
Article
Urban Studies
Miaoyi Li et al.
Summary: The study investigated the spatiotemporal distribution of street vibrancy and its built environment predictors in the Baitasi Area of Beijing, China, during summer/autumn and winter seasons. It found that street vibrancy in this area is relatively evenly distributed over time but more spatially concentrated, with microclimate and built environment playing a larger role in winter. Street morphology and configuration features were found to be more significant predictors of vibrancy than street function and landscape features, with higher diversity in points of interest, taller buildings, and stronger network connections correlating with higher vibrancy levels. This research provides valuable insights for decision makers aiming to revitalize historic districts.
Article
Urban Studies
Ahmed Jaad et al.
Summary: This study examines the urban growth patterns of five major cities in the MENA region and uses machine learning technology to generate accurate growth predictions, aiding in the development of sustainable growth policies.
Article
Computer Science, Interdisciplinary Applications
Wangyang Chen et al.
Summary: This paper introduces a new method based on deep learning to automatically visualize and classify road networks into four categories. By studying the road networks of nine global cities and uncovering latent city subgroups through clustering analysis, the research also found that road network classification has a positive impact on urban vitality.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Article
Environmental Sciences
Daniel Whitehurst et al.
Summary: This paper introduces a concept of using low-cost drones and adapted open-source software for aerial data collection, 3D cadastre modeling, and disaster risk assessment. Computer vision/machine learning methods are utilized to improve data accuracy and evaluate potential natural disaster risks.
Article
Computer Science, Artificial Intelligence
Peter Kok-Yiu Wong et al.
Summary: Analyzing the walking behavior of the public is crucial for infrastructure design and urban planning, where traditional manual surveys are labor-intensive but automated video analytics based on deep learning appear more efficient. However, existing methods of pedestrian tracking and attribute recognition face challenges, prompting the proposal of a more robust methodology and evaluation using benchmark datasets to show significant improvements.
ADVANCED ENGINEERING INFORMATICS
(2021)
Article
Engineering, Environmental
Anna C. O'Regan et al.
Summary: Studying street-level greenspace quantification, it was found that increased exposure to greenspace was associated with reported health status. Additionally, populations with higher greenspace exposure had higher income and education levels.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2021)
Article
Green & Sustainable Science & Technology
Tan Yigitcanlar et al.
Summary: This paper discusses the relationship between smart cities and artificial intelligence, emphasizing the importance of green AI and advocating for a more comprehensive AI approach to support the transformation of smart cities.
Article
Computer Science, Information Systems
Waishan Qiu et al.
Summary: The study integrated crowdsourcing, computer vision, and machine learning to subjectively measure perceptions suggested by classical urban design theory, achieving high accuracy in predicting scores. The results demonstrated a strong correlation between subjective measures and the density of urban amenities and services points of interest.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2021)
Article
Environmental Sciences
Deepank Verma et al.
Summary: Deep Learning (DL) is used to identify urban elements through Earth Observation (EO) datasets, focusing on fine-grained urban features in streetscapes, with the study evaluating model performance and generating geospatial datasets.
Article
Computer Science, Interdisciplinary Applications
Arianna Salazar Miranda et al.
Summary: The study uses pedestrian trajectories and built environment analysis to show that desirable streets provide better access to public amenities and have characteristics such as sinuosity and visual enclosure.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Liang Sun et al.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2020)
Article
Public, Environmental & Occupational Health
Jessica M. Keralis et al.
Article
Archaeology
Giacomo Landeschi et al.
ARCHAEOLOGICAL PROSPECTION
(2020)
Article
Multidisciplinary Sciences
Sagar Joglekar et al.
ROYAL SOCIETY OPEN SCIENCE
(2020)
Article
Environmental Sciences
Md Golam Mortoja et al.
Article
Architecture
Emily Schlickman
JOURNAL OF LANDSCAPE ARCHITECTURE
(2020)
Article
Environmental Studies
Leonardo Barleta et al.
Article
Remote Sensing
Yi Qi et al.
GEO-SPATIAL INFORMATION SCIENCE
(2020)
Article
Multidisciplinary Sciences
Lazar Ilic et al.
Review
Engineering, Multidisciplinary
Billie F. Spencer et al.
Article
Environmental Sciences
Philip Stubbings et al.
Article
Energy & Fuels
Debora Sotto et al.
Article
Green & Sustainable Science & Technology
Zhaoya Gong et al.
Article
Remote Sensing
Elena Ranguelova et al.
EUROPEAN JOURNAL OF REMOTE SENSING
(2019)
Article
Public, Environmental & Occupational Health
Quynh C. Nguyen et al.
PREVENTIVE MEDICINE REPORTS
(2019)
Article
Biodiversity Conservation
Roberta Arbolino et al.
ECOLOGICAL INDICATORS
(2018)
Article
Multidisciplinary Sciences
Salma Samiei et al.
Article
Geography
Valerie Maquil et al.
JOURNAL OF GEOGRAPHICAL SYSTEMS
(2018)
Article
Regional & Urban Planning
Dave Guyadeen et al.
JOURNAL OF PLANNING EDUCATION AND RESEARCH
(2018)
Article
Geography, Physical
Xiaojiang Li et al.
GISCIENCE & REMOTE SENSING
(2017)
Article
Ecology
Ian Seiferling et al.
LANDSCAPE AND URBAN PLANNING
(2017)
Article
Multidisciplinary Sciences
Nikhil Naik et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2017)
Article
Green & Sustainable Science & Technology
Sajida Perveen et al.
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Political Science
Michael D. M. Bader et al.
ANNALS OF THE AMERICAN ACADEMY OF POLITICAL AND SOCIAL SCIENCE
(2017)
Article
Computer Science, Interdisciplinary Applications
Lun Liu et al.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2017)
Article
Urban Studies
Tan Yigitcanlar et al.
JOURNAL OF URBAN TECHNOLOGY
(2016)
Article
Computer Science, Interdisciplinary Applications
Sebastian Muhs et al.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2016)
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Environmental Sciences
T. Yigitcanlar et al.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
(2015)
Article
Computer Science, Interdisciplinary Applications
Ludovico Carozza et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2014)
Proceedings Paper
Computer Science, Theory & Methods
Arnis Cirulis et al.
2013 INTERNATIONAL CONFERENCE ON VIRTUAL AND AUGMENTED REALITY IN EDUCATION
(2013)
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Computer Science, Artificial Intelligence
Arnold Irschara et al.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2012)
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Engineering, Civil
Junhao Zou et al.
KSCE JOURNAL OF CIVIL ENGINEERING
(2012)
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Engineering, Civil
J Shen et al.
JOURNAL OF URBAN PLANNING AND DEVELOPMENT-ASCE
(2001)
