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Ph.D. Student Email: [email protected], [email protected] School of Architecture, Southeast University Nanjing, China
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.
Chenyi Cai; Zifeng Guo; Baizhou Zhang; Xiao Wang; Biao Li; Peng Tang. Urban Morphological Feature Extraction and Multi-Dimensional Similarity Analysis Based on Deep Learning Approaches. Sustainability 2021, 13, 6859 .
AMA StyleChenyi Cai, Zifeng Guo, Baizhou Zhang, Xiao Wang, Biao Li, Peng Tang. Urban Morphological Feature Extraction and Multi-Dimensional Similarity Analysis Based on Deep Learning Approaches. Sustainability. 2021; 13 (12):6859.
Chicago/Turabian StyleChenyi Cai; Zifeng Guo; Baizhou Zhang; Xiao Wang; Biao Li; Peng Tang. 2021. "Urban Morphological Feature Extraction and Multi-Dimensional Similarity Analysis Based on Deep Learning Approaches." Sustainability 13, no. 12: 6859.
The significant challenges that urban design faces when moving forward into this new digital era involve the increasingly massive and complex projects that must be analyzed and developed. The need for efficient analysis tools and rational design methods represent ongoing concerns, while practicable and maneuverable applications rather than experimental studies are scarce. This research implemented a framework of digital description and generative grammar of block form from the perspective of block morphological complexity. The implemented framework was tested in an urban design practice. First, this study adapted the hierarchical structure and access structure theory to analyze the spatial form of target blocks. The characteristics of blocks case samples in Nanjing are extracted as text descriptions. Second, the relevant composition patterns and parameters were employed as features for classification and were converted into procedural rules. With the top-down control by rules and bottom-up generation by shape grammar in CityEngine, the texture of the block can be generated as close to the actual block as possible. Furthermore, in a real urban design case located in Nanjing, this work applies the method to construct a three-dimensional scene quickly and accurately. After integrating design factors such as an environment, transportation, and vision and summarizing the intentions of blocks and buildings in corresponding functions and control indexes, the initial generation plan was built by applying the obtained characteristics and procedural rules in specific shape grammar. Finally, designers can adjust the result in detail by employing real-time calculation and interactive operation.
Xiao Wang; Yacheng Song; Peng Tang. Generative urban design using shape grammar and block morphological analysis. Frontiers of Architectural Research 2020, 9, 914 -924.
AMA StyleXiao Wang, Yacheng Song, Peng Tang. Generative urban design using shape grammar and block morphological analysis. Frontiers of Architectural Research. 2020; 9 (4):914-924.
Chicago/Turabian StyleXiao Wang; Yacheng Song; Peng Tang. 2020. "Generative urban design using shape grammar and block morphological analysis." Frontiers of Architectural Research 9, no. 4: 914-924.
Peng Tang; Xiao Wang; Xing Shi. Generative design method of the facade of traditional architecture and settlement based on knowledge discovery and digital generation: a case study of Gunanjie Street in China. International Journal of Architectural Heritage 2018, 13, 679 -690.
AMA StylePeng Tang, Xiao Wang, Xing Shi. Generative design method of the facade of traditional architecture and settlement based on knowledge discovery and digital generation: a case study of Gunanjie Street in China. International Journal of Architectural Heritage. 2018; 13 (5):679-690.
Chicago/Turabian StylePeng Tang; Xiao Wang; Xing Shi. 2018. "Generative design method of the facade of traditional architecture and settlement based on knowledge discovery and digital generation: a case study of Gunanjie Street in China." International Journal of Architectural Heritage 13, no. 5: 679-690.