Urban Morphological Feature Extraction and Multi-Dimensional Similarity Analysis Based on Deep Learning Approaches

The study of urban morphology contributes to the evolution of cities and sustainable development. Urban morphological feature extraction and similarity analysis represents a practical framework in many studies to interpret and introduce the current built environment to aid in proposing novel designs. In conventional methods, morphological features are represented based on qualitative descriptions, symbolical interpretation, or manually selected indicators. However, these methods could cause subjective bias and limit the generalizability. This study proposes a hybrid data-driven approach to support quantitative morphological descriptions and multi-dimensional similarity analysis for urban design decision-making and to further morphology-related studies using information abundance via a deep-learning approach. We constructed a dataset of 3817 residential plots with geometrical and related infrastructure information. A deep convolutional neural network, GoogLeNet, was implemented with the plots’ figure–ground images, by quantifying the morphological features into 2048-dimensional feature vectors. We conducted a similarity analysis of the plots by calculating the Euclidean distance between the high-dimensional feature vectors. Then, a comparison study was performed by retrieving cases based on the plot shape and plots with buildings separately. The proposed method considers the overall characteristics of the urban morphology and social infrastructure situations for similarity analysis. This method is flexible and effective. The proposed framework indicates the feasibility and potential of integrating task-oriented information to introduce custom and adequate references via deep learning methods, which could support decision making and association studies on morphology with urban consequences. This work could serve as a basis for further typo-morphology studies and other morphology-related ecological, social, and economic studies for sustainable built environments.

[1]  Fei Chen.  Urban morphology and citizens life , 2014 .

[2]  Erwan Bocher,et al.  A geoprocessing framework to compute urban indicators: The MApUCE tools chain , 2018, Urban Climate.

[3]  Li Li,et al.  New Quantitative Approach for the Morphological Similarity Analysis of Urban Fabrics Based on a Convolutional Autoencoder , 2019, IEEE Access.

[4]  Hao Hua,et al.  A case-based design with 3D mesh models of architecture , 2014, Comput. Aided Des..

[5]  K. Steemers,et al.  Solar energy and urban morphology: Scenarios for increasing the renewable energy potential of neighbourhoods in London , 2015 .

[6]  Nizam Onur Sönmez,et al.  A review of the use of examples for automating architectural design tasks , 2018 .

[7]  Karl Kropf,et al.  Aspects of urban form , 2009, Urban Morphology.

[8]  Zifeng Guo,et al.  A framework for the management of agricultural resources with automated aerial imagery detection , 2019, Comput. Electron. Agric..

[9]  Q. Han,et al.  Urban morphology indicator analyzes for urban energy modeling , 2020 .

[10]  Lars Marcus,et al.  Towards a socio-ecological spatial morphology: integrating elements of urban morphology and landscape ecology , 2019, Urban Morphology.

[11]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Melissa M. Bilec,et al.  Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA , 2020, Buildings.

[14]  T. Ariga.  Morphology, Sustainable Evolution of Inner-urban Neighborhoods in San Francisco , 2005 .

[15]  Diana Alvarez-Marin,et al.  Indexical Cities: Articulating Personal Models of Urban Preference with Geotagged Data , 2020, ArXiv.

[16]  Srinath Perera,et al.  Case-based design: A review and analysis of building design applications , 1997, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[17]  Jefersson Alex dos Santos,et al.  Towards better exploiting convolutional neural networks for remote sensing scene classification , 2016, Pattern Recognit..

[18]  Jorge Gil,et al.  On the discovery of urban typologies: data mining the many dimensions of urban form , 2011, Urban Morphology.

[19]  J. A. Lopes Gil,et al.  Analyzing the Configuration of Multimodal Urban Networks , 2014 .

[20]  Luís M. A. Bettencourt,et al.  Why are large cities faster? Universal scaling and self-similarity in urban organization and dynamics , 2008 .

[21]  Kwonsik Song,et al.  Maintenance cost prediction for aging residential buildings based on case-based reasoning and genetic algorithm , 2020 .

[22]  Fei Chen.  Interpreting urban micromorphology in China: case studies from Suzhou , 2012, Urban Morphology.

[23]  T. Osman,et al.  Quantifying the Relationship between the Built Environment Attributes and Urban Sustainability Potentials for Housing Areas , 2016 .

[24]  Lesley Hemphill,et al.  An Indicator-based Approach to Measuring Sustainable Urban Regeneration Performance: Part 1, Conceptual Foundations and Methodological Framework , 2004 .