AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
The increasingly mature computer vision (CV) technology represented by convolutional neural networks (CNN) and available high-resolution remote sensing images (HR-RSIs) provide opportunities to accurately measure the evolution of natural and artificial environments on Earth at a large scale. Based on the advanced CNN method high-resolution net (HRNet) and multi-temporal HR-RSIs, a framework is proposed for monitoring a green evolution of courtyard buildings characterized by their courtyards being roofed (CBR). The proposed framework consists of an expert module focusing on scenes analysis, a CV module for automatic detection, an evaluation module containing thresholds, and an output module for data analysis. Based on this, the changes in the adoption of different CBR technologies (CBRTs), including light-translucent CBRTs (LT-CBRTs) and non-light-translucent CBRTs (NLT-CBRTs), in 24 villages in southern Hebei were identified from 2007 to 2021. The evolution of CBRTs was featured as an inverse S-curve, and differences were found in their evolution stage, adoption ratio, and development speed for different villages. LT-CBRTs are the dominant type but are being replaced and surpassed by NLT-CBRTs in some villages, characterizing different preferences for the technology type of villages. The proposed research framework provides a reference for the evolution monitoring of vernacular buildings, and the identified evolution laws enable to trace and predict the adoption of different CBRTs in a particular village. This work lays a foundation for future exploration of the occurrence and development mechanism of the CBR phenomenon and provides an important reference for the optimization and promotion of CBRTs.
Abioye SO, Oyedele LO, Akanbi L, et al. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44: 103299.
Arashpour M, Ngo T, Li H (2021). Scene understanding in construction and buildings using image processing methods: A comprehensive review and a case study. Journal of Building Engineering, 33: 101672.
Aronin JE (1953). Climate and Architecture. New York: Reinhold.
Badrinarayanan V, Kendall A, Cipolla R (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39: 2481–2495.
Che W, Cao Z, Shi Y, et al. (2022). Renewal and upgrading of a courtyard building in the historic and cultural district of Beijing: Design concept of 'multiple coexistence' and a case study. Indoor and Built Environment, 31: 522–536.
Chen F, Zhou F, Hao J (2004). Contemporary inheritance and development of the mode of traditional residence "Liang Shuai Xiu" courtyard house—Exploring the development of the mode of traditional residence in south Hebei province. New Architecture, 2004(04): 42–44. (in Chinese)
Chen P, Wu Z, Taciroglu E (2021a). Classification of soft-story buildings using deep learning with density features extracted from 3D point clouds. Journal of Computing in Civil Engineering, 35: 04021005.
Chen S, Li Y, Zhang T, et al. (2021b). Lunar features detection for energy discovery via deep learning. Applied Energy, 296: 117085.
Cheng G, Han J (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117: 11–28.
Cheng Y, Liu W, Xing W (2021). Weighted feature fusion and attention mechanism for object detection. Journal of Electronic Imaging, 30(2): 023015.
Choi H, Na H, Kim T, Kim T (2021a). Vision-based estimation of clothing insulation for building control: A case study of residential buildings. Building and Environment, 202: 108036.
Choi H, Um CY, Kang K, et al. (2021b). Application of vision-based occupancy counting method using deep learning and performance analysis. Energy and Buildings, 252: 111389.
Choi H, Um CY, Kang K, et al. (2021c). Review of vision-based occupant information sensing systems for occupant-centric control. Building and Environment, 203: 108064.
Dais D, Bal İE, Smyrou E, et al. (2021). Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning. Automation in Construction, 125: 103606.
Dan X, Ping S, Jiangwei K (2015). Investigation and analysis of traditional dwellings in Beijing-Tianjin-Hebei region. Architecture and Culture, 2015(06): 121–122. (in Chinese)
Darko A, Chan APC, Adabre MA, et al. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112: 103081.
Davari Majd R, Momeni M, Moallem P (2022). Generalizability in convolutional neural networks for various types of building scene recognition in High-Resolution imagery. Geocarto International, 37: 3565–3576.
Deng Z, Chen Y, Yang J, et al. (2022). Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets. Building Simulation, 15: 1547–1559.
Dong Z, Wang G, Amankwah SOY, et al. (2021). Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks. International Journal of Applied Earth Observation and Geoinformation, 102: 102400.
Fan C, Yan D, Xiao F, et al. (2021). Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches. Building Simulation, 14: 3–24.
Ghaffarian S, Kerle N, Pasolli E, et al. (2019). Post-disaster building database updating using automated deep learning: An integration of pre-disaster OpenStreetMap and multi-temporal satellite data. Remote Sensing, 11: 2427.
Guo H, Shi Q, Marinoni A, et al. (2021). Deep building footprint update network: A semi-supervised method for updating existing building footprint from bi-temporal remote sensing images. Remote Sensing of Environment, 264: 112589.
Han JH (2015). Transformation of the urban tissue and courtyard of residential architecture: with a focus on the discourses and plans of Paris in the 20th century. Journal of Asian Architecture and Building Engineering, 14: 435–442.
Hoeser T, Bachofer F, Kuenzer C (2020). Object detection and image segmentation with deep learning on earth observation data: A review—part II: applications. Remote Sensing, 12: 3053.
Hoffmann EJ, Wang Y, Werner M, et al. (2019). Model fusion for building type classification from aerial and street view images. Remote Sensing, 11: 1259.
Hu Q, Zhen L, Mao Y, et al. (2021). Automated building extraction using satellite remote sensing imagery. Automation in Construction, 123: 103509.
Jiang H, Peng M, Zhong Y, et al. (2022). A survey on deep learning-based change detection from high-resolution remote sensing images. Remote Sensing, 14: 1552.
Jin Y, Yan D, Zhang X, et al. (2021). A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development. Building Simulation, 14: 219–235.
Khoshboresh-Masouleh M, Alidoost F, Arefi H (2020). Multiscale building segmentation based on deep learning for remote sensing RGB images from different sensors. Journal of Applied Remote Sensing, 14(3): 034503.
Kim B, Yuvaraj N, Sri Preethaa KR, et al. (2020). Enhanced pedestrian detection using optimized deep convolution neural network for smart building surveillance. Soft Computing, 24: 17081–17092.
Kim B, Yuvaraj N, Sri Preethaa KR, et al. (2021). Surface crack detection using deep learning with shallow CNN architecture for enhanced computation. Neural Computing and Applications, 33: 9289–9305.
Koga Y, Miyazaki H, Shibasaki R (2020). A method for vehicle detection in high-resolution satellite images that uses a region-based object detector and unsupervised domain adaptation. Remote Sensing, 12: 575.
Li Y, Xu W, Chen H, et al. (2021). A novel framework based on mask R-CNN and histogram thresholding for scalable segmentation of new and old rural buildings. Remote Sensing, 13: 1070.
Liu YQ, Wang AL, Hou J, et al. (2020). Comprehensive evaluation of rural courtyard utilization efficiency: A case study in Shandong Province, Eastern China. Journal of Mountain Science, 17: 2280–2295.
Liu Y, Ning Q (2021). Triple understanding of Guanzhong Narrow Courtyard and its house space. Journal of Housing and the Built Environment, 36: 521–537.
Ma Y, Deng W, Xie J, et al. (2022). Generating prototypical residential building geometry models using a new hybrid approach. Building Simulation, 15: 17–28.
Meir IA, Pearlmutter D, Etzion Y (1995). On the microclimatic behavior of two semi-enclosed attached courtyards in a hot dry region. Building and Environment, 30: 563–572.
Meng C, Song Y, Ji J, et al. (2022). Automatic classification of rural building characteristics using deep learning methods on oblique photography. Building Simulation, 15: 1161–1174.
Miranda LCM, Lima CAS (2013). Technology substitution and innovation adoption: The cases of imaging and mobile communication markets. Technological Forecasting and Social Change, 80: 1179–1193.
Monna F, Rolland T, Denaire A, et al. (2021). Deep learning to detect built cultural heritage from satellite imagery—Spatial distribution and size of vernacular houses in Sumba, Indonesia. Journal of Cultural Heritage, 52: 171–183.
Nasrollahi N, Hatami M, Khastar SR, et al. (2017). Numerical evaluation of thermal comfort in traditional courtyards to develop new microclimate design in a hot and dry climate. Sustainable Cities and Society, 35: 449–467.
Neupane B, Horanont T, Aryal J (2021). Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis. Remote Sensing, 13: 808.
Oliver P (1997). Encyclopedia of Vernacular Architecture of the World. Cambridge, UK: Cambridge University Press.
Pan Y, Zhang L (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122: 103517.
Philokyprou M, Michael A (2021). Environmental sustainability in the conservation of vernacular architecture. The case of rural and urban traditional settlements in Cyprus. International Journal of Architectural Heritage, 15: 1741–1763.
Reichstein M, Camps-Valls G, Stevens B, et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743): 195–204.
Romani Z, Draoui A, Allard F (2022). Metamodeling and multicriteria analysis for sustainable and passive residential building refurbishment: A case study of French housing stock. Building Simulation, 15: 453–472.
Seong S, Choi J (2021). Semantic segmentation of urban buildings using a high-resolution network (HRNet) with channel and spatial attention gates. Remote Sensing, 13: 3087.
Shadar H (2013). The evolution of the inner courtyard in Israel: a reflection of the relationship between the Western modernist hegemony and the Mediterranean environment. Journal of Israeli History, 32: 51–74.
Shahin AI, Almotairi S (2021). DCRN: an optimized deep convolutional regression network for building orientation angle estimation in high-resolution satellite images. Electronics, 10: 2970.
Shen L, Lu Y, Chen H, et al. (2021). S2Looking: A satellite side-looking dataset for building change detection. Remote Sensing, 13: 5094.
Sun L, Tang Y, Zhang L (2017). Rural building detection in high-resolution imagery based on a two-stage CNN model. IEEE Geoscience and Remote Sensing Letters, 14: 1998–2002.
Tang R, Fan C, Zeng F, et al. (2022). Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression. Building Simulation, 15: 317–331.
Vargas Muñoz JE, Tuia D, Falcão AX (2021). Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient. International Journal of Geographical Information Science, 35: 1725–1745.
Verykokou S, Ioannidis C (2018). Oblique aerial images: a review focusing on georeferencing procedures. International Journal of Remote Sensing, 39: 3452–3496.
Wang F, Yu F, Zhu X, et al. (2016). Disappearing gradually and unconsciously in rural China: Research on the Sunken courtyard and the reasons for change in Shanxian County, Henan Province. Journal of Rural Studies, 47: 630–649.
Wang J, Sun K, Cheng T, et al. (2021). Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43: 3349–3364.
Wang Y, Li S, Teng F, et al. (2022). Improved mask R-CNN for rural building roof type recognition from UAV high-resolution images: A case study in Hunan Province, China. Remote Sensing, 14: 265.
Wen B, Musa N, Onn CC, et al. (2020a). Evolution of sustainability in global green building rating tools. Journal of Cleaner Production, 259: 120912.
Wen B, Musa SN, Onn CC, et al. (2020b). The role and contribution of green buildings on sustainable development goals. Building and Environment, 185: 107091.
Wu AN, Biljecki F (2021). Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability. Landscape and Urban Planning, 214: 104167.
Xu Z, Zhou Y, Wang S, et al. (2020). A novel intelligent classification method for urban green space based on high-resolution remote sensing images. Remote Sensing, 12: 3845.
Xu Y, Wu S, Guo M, et al. (2021). A framework for the evaluation of roof greening priority. Building and Environment, 206: 108392.
Yao X, Dewancker BJ, Guo Y, et al. (2020). Study on passive ventilation and cooling strategies for cold lanes and courtyard houses—A case study of rural traditional village in Shaanxi, China. Sustainability, 12: 8687.
Yazdi H, Vukorep I, Banach M, et al. (2021). Central courtyard feature extraction in remote sensing aerial images using deep learning: a case-study of Iran. Remote Sensing, 13: 4843.
Ye Z, Fu Y, Gan M, et al. (2019). Building extraction from very high resolution aerial imagery using joint attention deep neural network. Remote Sensing, 11: 2970.
Ye Y, Hinkelman K, Lou Y, et al. (2021). Evaluating the energy impact potential of energy efficiency measures for retrofit applications: A case study with US medium office buildings. Building Simulation, 14: 1377–1393.
Yi YK, Zhang Y, Myung J (2020). House style recognition using deep convolutional neural network. Automation in Construction, 118: 103307.
Yuan X, Shi J, Gu L (2021). A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Systems with Applications, 169: 114417.
Zhang L, Leach M (2022). Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation. Building Simulation, 15: 769–778.
Zhu L, Wang B, Sun Y (2020). Multi-objective optimization for energy consumption, daylighting and thermal comfort performance of rural tourism buildings in north China. Building and Environment, 176: 106841.
Zolfagharkhani M, Ostwald MJ (2021). The spatial structure of Yazd courtyard houses: A space syntax analysis of the topological characteristics of the courtyard. Buildings, 11: 262.
