Identification and Extracting Method of Exterior Building Information on 3D Map

Shon, Donghwa; Noh, Byeongjoon; Byun, Nahyang

doi:10.3390/buildings12040452

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…With the improvement of computer hardware facilities, deep learning technology is increasingly used in the field of construction, such as in building disaster prevention [22][23][24][25][26], building energy consumption [27][28][29][30], and building extraction [31,32]. In recent years, the application of deep learning technology has also appeared in the fields of building façade color research [33], building management [34], and architectural psychology [35].…”

Section: Deep Learning Technology In the Field Of Architectural Studymentioning

confidence: 99%

Urban Architectural Style Recognition and Dataset Construction Method under Deep Learning of Street View Images: A Case Study of Wuhan

Sun

Wang

et al. 2023

IJGI

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The visual quality and spatial distribution of architectural styles represent a city’s image, influence inhabitants’ living conditions, and may have positive or negative social consequences which are critical to urban sensing and designing. Conventional methods of identifying architectural styles rely on human labor and are frequently time-consuming, inefficient, and subjective in judgment. These issues significantly affect the large-scale management of urban architectural styles. Fortunately, deep learning models have robust feature expression abilities for images and have achieved highly competitive results in object detection in recent years. They provide a new approach to supporting traditional architectural style recognition. Therefore, this paper summarizes 22 architectural styles in a study area which could be used to define and describe urban architectural styles in most Chinese urban areas. Then, this paper introduced a Faster-RCNN general framework of architectural style classification with a VGG-16 backbone network, which is the first machine learning approach to identifying architectural styles in Chinese cities. Finally, this paper introduces an approach to constructing an urban architectural style dataset by mapping the identified architectural style through continuous street view imagery and vector map data from a top-down building contour map. The experimental results show that the architectural style dataset created had a precision of 57.8%, a recall rate of 80.91%, and an F1 score of 0.634. This dataset can, to a certain extent, reflect the geographical distribution characteristics of a wide variety of urban architectural styles. The proposed approach could support urban design to improve a city’s image.

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Section: Deep Learning Technology In the Field Of Architectural Studymentioning

confidence: 99%

Urban Architectural Style Recognition and Dataset Construction Method under Deep Learning of Street View Images: A Case Study of Wuhan

Sun

Wang

et al. 2023

IJGI

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Use of artificial neural networks in architecture: determining the architectural style of a building with a convolutional neural networks

Cantemir,

Kandemir

2024

Neural Comput & Applic

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The discussion of "can machines think?" which started with the invention of the modern computer, brought along the question of "can machines design?" by researchers in the design field. These developments in information technologies have also affected the architecture. Artificial intelligence applications are encountered in many areas such as pricing estimation, energy conservation security systems of buildings, ventilation systems, user-oriented interactive design solutions, computer-aided programs used in the plan production phase and design process. When the literature on artificial intelligence applications in the architecture is reviewed, it can be seen that it generally includes shape grammars, graph theory, decision trees, constraint-based models, machine learning methods, RNN (Recursive Neural Networks), CNN (Convolutional Neural Network) and GAN (Generative Adversarial Network) algorithms. In this study, the use of artificial intelligence algorithms in architecture was examined, and an example was designed to determine the architectural structures of different periods by using CNN (Convolutional Neural Network). In the study, the open source TensorFlow library developed by Google and the Python programming language were used. Employing a statistical approach and utilizing convolutional neural networks (CNNs), a study has successfully classified the current flow patterns of buildings based on datasets comprising facades of Gothic, Modern, and Deconstructivist architectural styles. The findings demonstrate the efficacy of CNNs in accurately distinguishing the intricate details of diverse architectural styles. Recognizing elements from different periods using the CNN algorithm can examine not only individual buildings but also the relationship of buildings with their environments. It can also gain an important place in the field of conservation of the architectural discipline. The historical processes, aesthetic features and changes of protected buildings can be learned with the CNN algorithm and can guide restoration decisions. As a result of the study, the employed CNN-based model can correctly classify structures with 84.66% accuracy rate.

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Artificial intelligence for deconstruction: Current state, challenges, and opportunities

Balogun,

Alaka,

Demir

et al. 2024

Automation in Construction

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Identification and Extracting Method of Exterior Building Information on 3D Map

Cited by 7 publications

References 23 publications

Urban Architectural Style Recognition and Dataset Construction Method under Deep Learning of Street View Images: A Case Study of Wuhan

Urban Architectural Style Recognition and Dataset Construction Method under Deep Learning of Street View Images: A Case Study of Wuhan

Use of artificial neural networks in architecture: determining the architectural style of a building with a convolutional neural networks

Artificial intelligence for deconstruction: Current state, challenges, and opportunities

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