Prediction of summer daytime land surface temperature in urban environments based on machine learning

[1]  Jiayin Zhu,et al.  A review of the influence of courtyard geometry and orientation on microclimate , 2023, Building and Environment.

[2]  Xiangming Xiao,et al.  Relationship between urban spatial form and seasonal land surface temperature under different grid scales , 2022, Sustainable Cities and Society.

[3]  Arijit Das,et al.  Urbanization induced degradation of urban green space and its association to the land surface temperature in a medium-class city in India , 2022, Sustainable Cities and Society.

[4]  Wei Chen,et al.  The spatial configuration of buildings: A vital consideration impossible to ignore in regulating urban land surface temperature? Evidence from 35 Chinese cities. , 2022, The Science of the total environment.

[5]  B. Zuckerberg,et al.  Urban greenspaces promote warmer soil surface temperatures in a snow-covered city , 2022, Landscape and Urban Planning.

[6]  Hayder Algretawee.  The effect of graduated urban park size on park cooling island and distance relative to land surface temperature (LST) , 2022, Urban Climate.

[7]  F. Kharvari,et al.  Impact of extended reality on architectural education and the design process , 2022, Automation in Construction.

[8]  H. Bagan,et al.  Quantitative analysis of the building-level relationship between building form and land surface temperature using airborne LiDAR and thermal infrared data , 2022, Urban Climate.

[9]  Zhanghua Xu,et al.  Investigating the effects of urban morphological factors on seasonal land surface temperature in a “Furnace city” from a block perspective , 2022, Sustainable Cities and Society.

[10]  Wenze Yue,et al.  Divergent impact of urban 2D/3D morphology on thermal environment along urban gradients , 2022, Urban Climate.

[11]  D. Edwards,et al.  Post- occupancy evaluation: Identifying and mitigating implementation barriers to reduce environmental impact , 2022, Journal of Cleaner Production.

[12]  Hao Zheng,et al.  Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic , 2022, International Journal of Applied Earth Observation and Geoinformation.

[13]  Zullyadini A. Rahaman,et al.  Modelling the mpacts of land use/land cover changing pattern on urban thermal characteristics n Kuwait , 2022, Sustainable Cities and Society.

[14]  Muhammad Tauhidur Rahman,et al.  Assessing the impacts of vegetation cover loss on surface temperature, urban heat island and carbon emission in Penang city, Malaysia , 2022, Building and Environment.

[15]  Bashir Adamu,et al.  Modelling land surface temperature in urban areas using spatial regression models , 2022, Urban Climate.

[16]  Han Jiang,et al.  Scientometric Analysis of Post-Occupancy Evaluation Research: Development, Frontiers and Main Themes , 2022, Energy and Buildings.

[17]  Y. Murayama,et al.  Prediction of the future urban heat island intensity and distribution based on landscape composition and configuration: A case study in Hangzhou , 2022, Sustainable Cities and Society.

[18]  Z. El-Khatib,et al.  Extreme weather events and death based on temperature and CO2 emission – A global retrospective study in 77 low-, middle- and high-income countries from 1999 to 2018 , 2022, Preventive medicine reports.

[19]  E. Njoku,et al.  Quantitative assessment of the relationship between land use land cover (LULC), topographic elevation, and land surface temperature (LST) in Ilorin, Nigeria , 2022, Remote Sensing Applications: Society and Environment.

[20]  Xiaoxia Zhou,et al.  An evidence-based framework for designing urban green infrastructure morphology to reduce urban building energy in a hot-humid climate , 2022, Building and Environment.

[21]  Shahab E. Jozdani,et al.  A review and meta-analysis of Generative Adversarial Networks and their applications in remote sensing , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Yin Shan,et al.  Quantitative analysis and prediction of urban heat island intensity on urban-rural gradient: A case study of Shanghai. , 2022, The Science of the total environment.

[23]  J. Scartezzini,et al.  Combining computational fluid dynamics and neural networks to characterize microclimate extremes: Learning the complex interactions between meso-climate and urban morphology. , 2022, The Science of the total environment.

[24]  Ajanta Goswami,et al.  Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India , 2022, Urban Climate.

[25]  Y. E. Ghajari,et al.  Improving Land Surface Temperature Prediction using Spatiotemporal Factors through a Genetic-Based Selection Procedure (Case Study: Tehran, Iran) , 2022, Advances in Space Research.

[26]  Xiaona Wang,et al.  The dominant factors and influence of urban characteristics on land surface temperature using random forest algorithm , 2022, Sustainable Cities and Society.

[27]  Majid Amani-Beni,et al.  Quantitative-spatial relationships between air and surface temperature, a proxy for microclimate studies in fine-scale intra-urban areas? , 2021, Sustainable Cities and Society.

[28]  H. Soltanifard,et al.  Analysis of Spatially Varying Relationships Between Urban Environment Factors And Land Surface Temperature In Mashhad City, Iran , 2021, The Egyptian Journal of Remote Sensing and Space Science.

[29]  Jingyi He,et al.  Prediction of crime rate in urban neighborhoods based on machine learning , 2021, Eng. Appl. Artif. Intell..

[30]  J. Jagannathan,et al.  Deep learning for the prediction and classification of land use and land cover changes using deep convolutional neural network , 2021, Ecol. Informatics.

[31]  K. Ezimand,et al.  Analyzing the effects of 2D and 3D urban structures on LST changes using remotely sensed data , 2021 .

[32]  Zhen Li,et al.  Exploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China , 2021, Sustainable Cities and Society.

[33]  Sakib Shahriar,et al.  GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network , 2021, Displays.

[34]  Zullyadini A. Rahaman,et al.  Modeling the relationship between land use/land cover and land surface temperature in Dhaka, Bangladesh using CA-ANN algorithm , 2021, Environmental Challenges.

[35]  A. Kafy,et al.  Assessment and prediction of seasonal land surface temperature change using multi-temporal Landsat images and their impacts on agricultural yields in Rajshahi, Bangladesh , 2021 .

[36]  Cédric Stéphane Tékouabou Koumétio,et al.  Exploring the relationship between urban form and land surface temperature (LST) in a semi-arid region Case study of Ben Guerir city - Morocco , 2021 .

[37]  Haowei Mu,et al.  Separate and combined effects of 3D building features and urban green space on land surface temperature. , 2021, Journal of environmental management.

[38]  R. Zandi,et al.  Evaluation of the effect of geographical parameters on the formation of the land surface temperature by applying OLS and GWR, A case study Shiraz City, Iran , 2021 .

[39]  Atiqur Rahman,et al.  Impervious surface growth and its inter-relationship with vegetation cover and land surface temperature in peri-urban areas of Delhi , 2021 .

[40]  S. Dhakal,et al.  Future urban expansion and local climate zone changes in relation to land surface temperature: Case of Bangkok Metropolitan Administration, Thailand , 2021 .

[41]  Chuan-Yu Chang,et al.  Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis , 2021, Complex..

[42]  Mohanad Hassan Edan,et al.  Predicting the impacts of land use/land cover change on land surface temperature using remote sensing approach in Al Kut, Iraq , 2021 .

[43]  Arijit Das,et al.  Analyzing spatial relationship between land use/land cover (LULC) and land surface temperature (LST) of three urban agglomerations (UAs) of Eastern India , 2021 .

[44]  M. Galleguillos,et al.  Effect of urban tree diversity and condition on surface temperature at the city block scale , 2021 .

[45]  Qingping Zhang,et al.  Predicting the surface urban heat island intensity of future urban green space development using a multi-scenario simulation , 2021 .

[46]  Ido Nevat,et al.  Climate-informed decision-making for urban design: Assessing the impact of urban morphology on urban heat island , 2021 .

[47]  Weiqi Zhou,et al.  Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature , 2021 .

[48]  V. Rana,et al.  Quantifying the effects of urban land forms on land surface temperature and modelling the spatial variation using machine learning , 2020, Geocarto International.

[49]  Fengyun Sun,et al.  The effects of 3D architectural patterns on the urban surface temperature at a neighborhood scale: Relative contributions and marginal effects , 2020 .

[50]  Jianjun Zhang,et al.  Effects of building density on land surface temperature in China: Spatial patterns and determinants , 2020 .

[51]  Touseef Iqbal,et al.  The survey: Text generation models in deep learning , 2020, J. King Saud Univ. Comput. Inf. Sci..

[52]  Esmaeil Zarghami,et al.  Spatio-temporal planning of urban neighborhoods in the context of global climate change: Lessons for urban form design in Tehran, Iran , 2019, Sustainable Cities and Society.

[53]  Doha Eissa,et al.  Concept generation in the architectural design process: A suggested hybrid model of vertical and lateral thinking approaches , 2019, Thinking Skills and Creativity.

[54]  R. Kumar,et al.  Prediction of land surface temperatures for surface urban heat island assessment over Chandigarh city using support vector regression model , 2019, Solar Energy.

[55]  Yimin Zhou,et al.  A Review: Generative Adversarial Networks , 2019, 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[56]  Puay Yok Tan,et al.  Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature , 2019, Landscape and Urban Planning.

[57]  Hao Chen,et al.  RNN-based default logic for route planning in urban environments , 2019, Neurocomputing.

[58]  Mohammad Karimi Firozjaei,et al.  Statistical analysis of surface urban heat island intensity variations: A case study of Babol city, Iran , 2018, GIScience & Remote Sensing.

[59]  Sulaiman Ibrahim Musa,et al.  A review of geospatial-based urban growth models and modelling initiatives , 2017 .

[60]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[61]  Sepp Hochreiter,et al.  GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.

[62]  Mojtaba Parsaee,et al.  Interactive architectural approach (interactive architecture): An effective and adaptive process for architectural design , 2016 .

[63]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[64]  Derya Ozturk,et al.  Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models , 2015, Remote. Sens..

[65]  Manuela Ruiz-Montiel,et al.  Randomness and control in design processes: An empirical study with architecture students , 2014 .

[66]  Aaron C. Courville,et al.  Generative adversarial networks , 2014, Commun. ACM.

[67]  Rosnaini Mahmud,et al.  Digital studio vs. conventional in teaching architectural design process , 2012 .

[68]  Zhiqiang Zhao,et al.  Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression , 2010, Environ. Model. Softw..

[69]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[70]  Halime Demirkan,et al.  Focus on architectural design process through learning styles , 2003 .

[71]  Silvia de Schiller,et al.  Training architects and planners to design with urban microclimates , 1996 .

[72]  Wan-yu Shih.  Socio-ecological inequality in heat: The role of green infrastructure in a subtropical city context , 2022, Landscape and Urban Planning.

[73]  T. Honjo,et al.  The prediction of city expansion and land surface temperature in Bogor City, Indonesia , 2020 .

[74]  Hao Zheng,et al.  Machine Learning Assisted Urban Filling , 2020, CAADRIA proceedings.

[75]  B. Dousset,et al.  Surface Temperatures in the Urban Environment , 2019, Taking the Temperature of the Earth.

[76]  Hao Zheng,et al.  Architectural Drawings Recognition and Generation through Machine Learning , 2018, Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA).

[77]  Mohamad Kashef,et al.  Architects and planners approaches to urban form and design in the Toronto region: A comparative analysis , 2008 .

[78]  Walter Jetz,et al.  Local and global approaches to spatial data analysis in ecology , 2005 .