A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A deep learning method for building façade parsing utilizing improved SOLOv2 instance segmentation
https://orcid.org/0000-0002-5473-1605
https://orcid.org/0000-0002-3094-5545
Highlights Proposed an automated deep learning-based framework for building facade parsing. Provided an enhanced feature extraction algorithm utilizing BiFPN and SE module. Dealt with the issue of occlusion and reflection based on the improved SOLOv2. Achieved an accurate estimation of WWR with a mAP of 93% for window segmentation. Discussed the effect of WWR granularity on energy simulation.
Abstract Energy consumption simulation and renovation of existing buildings require accurate acquisition of building façade features which mostly relies on time-consuming manual calculations based on architectural drawings. In this article, we proposed an automated deep learning-based approach based on the SE module and BiFPN to achieve precise and efficient façade feature extraction. The approach eliminated the image distortion of building façades and then enabled accurate segmentation of windows and accessory structures even under the situation of occlusion and reflection. The improved SOLOv2 algorithm resulted in a high mean average precision of 93% for window segmentation, leading to a more precise window-to-wall ratio estimation with a mean absolute error of 2.9% than the experts’ estimation and existing deep learning-based methods. Considering the accurate results of façade parsing, our method can be utilized for city-level building feature extraction, providing theoretical and practical references for urban building energy simulation, urban renewal, and building health examination.
A deep learning method for building façade parsing utilizing improved SOLOv2 instance segmentation
https://orcid.org/0000-0002-5473-1605
https://orcid.org/0000-0002-3094-5545
Highlights Proposed an automated deep learning-based framework for building facade parsing. Provided an enhanced feature extraction algorithm utilizing BiFPN and SE module. Dealt with the issue of occlusion and reflection based on the improved SOLOv2. Achieved an accurate estimation of WWR with a mAP of 93% for window segmentation. Discussed the effect of WWR granularity on energy simulation.
Abstract Energy consumption simulation and renovation of existing buildings require accurate acquisition of building façade features which mostly relies on time-consuming manual calculations based on architectural drawings. In this article, we proposed an automated deep learning-based approach based on the SE module and BiFPN to achieve precise and efficient façade feature extraction. The approach eliminated the image distortion of building façades and then enabled accurate segmentation of windows and accessory structures even under the situation of occlusion and reflection. The improved SOLOv2 algorithm resulted in a high mean average precision of 93% for window segmentation, leading to a more precise window-to-wall ratio estimation with a mean absolute error of 2.9% than the experts’ estimation and existing deep learning-based methods. Considering the accurate results of façade parsing, our method can be utilized for city-level building feature extraction, providing theoretical and practical references for urban building energy simulation, urban renewal, and building health examination.
A deep learning method for building façade parsing utilizing improved SOLOv2 instance segmentation
https://orcid.org/0000-0002-5473-1605
https://orcid.org/0000-0002-3094-5545
Highlights Proposed an automated deep learning-based framework for building facade parsing. Provided an enhanced feature extraction algorithm utilizing BiFPN and SE module. Dealt with the issue of occlusion and reflection based on the improved SOLOv2. Achieved an accurate estimation of WWR with a mAP of 93% for window segmentation. Discussed the effect of WWR granularity on energy simulation.
Abstract Energy consumption simulation and renovation of existing buildings require accurate acquisition of building façade features which mostly relies on time-consuming manual calculations based on architectural drawings. In this article, we proposed an automated deep learning-based approach based on the SE module and BiFPN to achieve precise and efficient façade feature extraction. The approach eliminated the image distortion of building façades and then enabled accurate segmentation of windows and accessory structures even under the situation of occlusion and reflection. The improved SOLOv2 algorithm resulted in a high mean average precision of 93% for window segmentation, leading to a more precise window-to-wall ratio estimation with a mean absolute error of 2.9% than the experts’ estimation and existing deep learning-based methods. Considering the accurate results of façade parsing, our method can be utilized for city-level building feature extraction, providing theoretical and practical references for urban building energy simulation, urban renewal, and building health examination.
A deep learning method for building façade parsing utilizing improved SOLOv2 instance segmentation
Lu, Yujie (author) / Wei, Wei (author) / Li, Peixian (author) / Zhong, Tao (author) / Nong, Yuanjun (author) / Shi, Xing (author)
Energy and Buildings ; 295
2023-06-15
Article (Journal)
Electronic Resource
English
Building facade evaluation using instance segmentation on thermal images
| DataCite | 2024
Deep Learning-based Segmentation of Thermal Images for Heat Loss Detection in Building Façade
| Taylor & Francis Verlag | 2024
Residential building facade segmentation in the urban environment
| Elsevier | 2021