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Obeso, Abraham Montoya; Vázquez, Mireya Saraí García; Acosta, Alejandro; Benois-Pineau, Jenny

doi:10.1145/3095713.3095730

Cited by 25 publications

(5 citation statements)

References 12 publications

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“…Llamas et al [3] classified architectural heritage photos using convolutional neural networks (CNN) and said these techniques can help digitally document architectural heritage. Obeso et al [4] used convolutional neural networks to classify building architectural styles in digital photographs of Mexican cultural heritage. This technique can improve video description tasks, especially automatic cultural heritage documentation.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks For Classification of Satellite Images

Bihari Nandan Pandey

2023

IJRITCC

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Deep learning algorithms that can learn from image, video, audio, and text data are becoming more successful as hardware power increases. Given the effectiveness and benefits of deep learning in many domains with more data, architecture should see similar implications. This study examined textures using particular rather than overall images. The deep convolutional neural network model classified 4500 satellite photos of clouds, deserts, greenery, and water. The constructed model classified previously unused test data (675 images) with 0.97 accuracies for cloud images, 0.98 for desert images, 0.96 for green areas, and 0.98 for water bodies. Although cloud and desert photos and green and water body images are comparable, this textural success shows that it can detect, analyze, and classify architectural elements. Deep convolutional neural networks can recognize, analyze, and classify architectural materials and elements, enabling shape recognition among many data to help architects collect helpful information. Thus, it will provide more extensive data than manual data analysis, enabling more accurate decisions. Understanding deep convolutional neural network data categorization characteristics explains architectural design differences and similarities. This condition reveals the hidden relationship in designs, allowing architects to create unique designs..

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks For Classification of Satellite Images

Bihari Nandan Pandey

2023

IJRITCC

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“…Llamas et al, [3] used convolutional neural networks (CNN) for classification of architectural heritage images and stated that the application of these techniques can significantly contribute to the digital documentation of architectural heritage. Similarly, Obeso et al, [4] used convolutional neural network for classification of architectural styles of buildings in digital photographs of Mexican cultural heritage and stated that style identification with this technique can make a wide contribution in video description tasks, especially in automatic documentation of cultural heritage.…”

Section: Introductionmentioning

confidence: 99%

Classification of Satellite Images With Deep Convolutional Neural Networks and Its Effect on Architecture

Diker

Erkan

2022

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Unlike traditional machine learning methods, deep learning methods that can learn from image, video, audio, and text data, especially recently with the increase in hardware power, are also increasing in success. Considering the success and benefits of deep learning methods in many different fields with increasing data, similar effects are expected in architecture. In this study, we focused on textures by going down to specifics rather than general images. In this direction, a total of 4500 satellite images belonging to cloud, desert, green areas and water bodies were classified in the model developed using deep convolutional neural networks. In the developed model, 0.97 accuracy for cloud images, 0.98 accuracy for desert images, 0.96 accuracy for green areas images and 0.98 accuracy for water bodies images were obtained in the classification of previously unused test data (675 images). Although there are similarities in the images of cloud and desert, and images of green areas and water bodies, this success in textures shows that it can be successful in detecting, analyzing, and classifying architectural materials. Successful recognition, analysis and classification of architectural materials and elements with deep convolutional neural networks will be able to facilitate the acquisition of appropriate and useful data through shape recognition among many data, especially at the information collection phase in the architectural design process. Thus, it will help to take more accurate decisions by obtaining more comprehensive data that cannot be obtained from manual data analysis. Learning the distinctive features for classification of data in deep convolutional neural networks also explains architectural design differences and similarities. This situation reveals the hidden relationship in the designs and thus can offer architects the opportunity to make creative and original designs.

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“…17,18 Deep learning and CNN have applications in ancient architecture as well. Llamas et al 19,20 Belhi et al 21 and Obeso et al 22 used CNN to classify heritage buildings. Pierdicca et al 23 used deep learning for semantic segmentation of 3D point clouds of ancient architectures.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation, inpainting, and classification of defective patterns on ancient architecture using multiple deep learning algorithms

Zou

Zhao

2021

Struct Control Health Monit

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Ancient architectures have a lot of distinctive patterns on their component surfaces. But due to the wind and the Sun, the patterns will present extensive deficiencies, which are not conducive to the routine inspections and the subsequent repair work. To overcome these limits, an automatic deep learning-based method of segmentation, inpainting, and classification for defective patterns on ancient architecture is proposed. First, You Only Look At CoefficienTs, which is a real-time instance segmentation network, is applied to obtain the mask of the defective parts. Then Generative Image Inpainting with Contextual Attention, which is an image inpainting algorithm, is used to reconstruct the defective parts. Finally, Residual Neural Network-50 is used to classify the reconstructed images. In this paper, three types of dragon motifs in the Forbidden City were studied. The results show that the classification accuracy of the reconstructed images is increased by an average of 13.1%, with the maximum increasing by 23.5%. The proposed methods can prepare for future routine inspections in advance.

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Connoisseur

Cited by 25 publications

References 12 publications

Deep Convolutional Neural Networks For Classification of Satellite Images

Deep Convolutional Neural Networks For Classification of Satellite Images

Classification of Satellite Images With Deep Convolutional Neural Networks and Its Effect on Architecture

Automatic segmentation, inpainting, and classification of defective patterns on ancient architecture using multiple deep learning algorithms

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