Abstract
This paper presents an automated as-is façade modeling method for existing and historic high-rise buildings, named Scan4Façade. To begin with, a camera drone with a spiral path is employed to capture building exterior images, and photogrammetry is used to conduct three-dimensional (3D) reconstruction and create mesh models for the scanned building façades. High-resolution façade orthoimages are then generated from mesh models and pixelwise segmented by an artificial intelligence (AI) model named U-net. A combined data augmentation strategy, including random flipping, rotation, resizing, perspective transformation, and color adjustment, is proposed for model training with a limited number of labels. As a result, the U-net achieves an average pixel accuracy of 0.9696 and a mean intersection over union of 0.9063 in testing. Then, the developed twoStagesClustering algorithm, with a two-round shape clustering and a two-round coordinates clustering, is used to precisely extract façade elements’ dimensions and coordinates from façade orthoimages and pixelwise label. In testing with the Michigan Central Station (office tower), a historic high-rise building, the developed algorithm achieves an accuracy of 99.77% in window extraction. In addition, the extracted façade geometric information and element types are transformed into AutoCAD command and script files to create CAD drawings without manual interaction. Experimental results also show that the proposed Scan4Façade method can provide clear and accurate information to assist BIM feature creation in Revit. Future research recommendations are also stated in this paper.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The deep learning workstation was financially supported by the McShane Endowment Fund at Marquette University. The authors are grateful to the reviewers for their valuable comments and feedback.
References
Abdelgawad, K., Y. A. Salman, and M. Shalaby. 2017. “Creating 2D/3D as-built models from point cloud for huge special-use buildings.” Autodesk University. Accessed January 24, 2021. https://www.autodesk.com/autodesk-university/class/Creating-2D3D-Built-Models-Point-Cloud-Huge-Special-Use-Buildings-2017.
Agapaki, E., and I. Brilakis. 2020. “CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets.” Adv. Eng. Inf. 45 (Nov): 101121. https://doi.org/10.1016/j.aei.2020.101121.
Ali, L., N. K. Valappil, D. N. A. Kareem, M. J. John, and H. Al Jassmi. 2019. “Pavement crack detection and localization using convolutional neural networks (CNNs).” In Proc., Int. Conf. on Digitization, 217–221. New York: IEEE.
Alipour, M., D. K. Harris, and G. R. Miller. 2019. “Robust pixel-level crack detection using deep fully convolutional neural networks.” J. Comput. Civ. Eng. 33 (6): 04019040. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000854.
Alsadik, B., and F. Remondino. 2020. “Flight planning for LiDAR-based UAS mapping applications.” ISPRS Int. J. Geo-Inf. 9 (6): 378. https://doi.org/10.3390/ijgi9060378.
Andrich, W., B. Daniotti, A. Pavan, and C. Mirarchi. 2022. “Check and validation of building information models in detailed design phase: A check flow to pave the way for BIM based renovation and construction processes.” Buildings 12 (2): 154. https://doi.org/10.3390/buildings12020154.
Augustaukas, R., and A. Lipnickas. 2019. “Pixel-wise road pavement defects detection using U-net deep neural network.” In Proc., 10th IEEE Int. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 468–471. New York: IEEE.
Badrinarayanan, V., A. Kendall, and R. Cipolla. 2017. “Segnet: A deep convolutional encoder–decoder architecture for image segmentation.” IEEE Trans. Pattern Anal. Mach. Intell. 39 (12): 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615.
Bassier, M., M. Vergauwen, and B. Van Genechten. 2016. “Automated semantic labelling of 3D vector models for scan-to-BIM.” In Proc., Annual Int. Conf. on Architecture and Civil Engineering, 93–100. Leuven, Belgium: KU Leuven.
Bassier, M., M. Vergauwen, and B. Van Genechten. 2017. “Automated classification of heritage buildings for as-built BIM using machine learning techniques.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. IV-2/W2 (2W2): 25–30. https://doi.org/10.5194/isprs-annals-IV-2-W2-25-2017.
Bolourian, N., and A. Hammad. 2020. “LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection.” Autom. Constr. 117 (Apr): 103250. https://doi.org/10.1016/j.autcon.2020.103250.
Cai, Z., Y. Lin, J. Li, Z. Zhang, and X. Huang. 2021. “Building facade completion using semantic-synchronized GAN.” In Proc., IEEE Int. Geoscience and Remote Sensing Symp., 6387–6390. New York: IEEE.
Chen, J., Z. Kira, and Y. K. Cho. 2019. “Deep learning approach to point cloud scene understanding for automated scan to 3D reconstruction.” J. Comput. Civ. Eng. 33 (4): 04019027. https://doi.org/10.1061/(asce)cp.1943-5487.0000842.
Chen, K., G. Reichard, A. Akanmu, and X. Xu. 2021a. “Geo-registering UAV-captured close-range images to GIS-based spatial model for building façade inspections.” Autom. Constr. 122 (Jan 2020): 103503. https://doi.org/10.1016/j.autcon.2020.103503.
Chen, K., G. Reichard, X. Xu, and A. Akanmu. 2021b. “Automated crack segmentation in close-range building façade inspection images using deep learning techniques.” J. Build. Eng. 43 (Nov 2020): 102913. https://doi.org/10.1016/j.jobe.2021.102913.
Chen, L. C., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. “Encoder–decoder with atrous separable convolution for semantic image segmentation.” In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), edited by V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, 833–851. Cham, Switzerland: Springer.
Choe, J., K. Joo, F. Rameau, and I. So Kweon. 2021. “Stereo object matching network.” In Proc., IEEE Int. Conf. on Robotics and Automation, 12918–12924. New York: IEEE.
Chollet, F. 2020a. “Concatenate layer.” Accessed June 13, 2020. https://keras.io/api/layers/merging_layers/concatenate/.
Chollet, F. 2020b. “Image segmentation with a U-Net-like architecture.” Accessed July 1, 2020. https://keras.io/examples/vision/oxford_pets_image_segmentation/.
Chollet, F. 2020c. “Accuracy metrics.” Accessed July 1, 2020. https://keras.io/api/metrics/accuracy_metrics/.
City and County of San Francisco. 2021. “Facade inspection and maintenance program.” Accessed February 18, 2022. https://sf.gov/facade-inspection-and-maintenance-program.
City of Cleveland. 2022. “Building & housing ordinances.” Accessed February 21, 2022. https://www.clevelandohio.gov/CityofCleveland/Home/Government/CityAgencies/BuildingHousing/Ordinances.
City of Milwaukee. 2008. “Façade building list.” Accessed February 19, 2022. http://www.city.milwaukee.gov/ImageLibrary/User/tweile/pdf/docs/FacadeList6_9_08.pdf.
City of Milwaukee. 2022. “Facade inspection ordinance.” Accessed February 19, 2022. https://city.milwaukee.gov/DNS/Inspections_Sections/commercial/Façade.
ClearEdge3D. 2021a. “Case study: MEP remodel in a historic hotel—Digital surveys uses EdgeWise MEP to cut modeling time by 70%.” Accessed February 20, 2022. https://www.clearedge3d.com/wp-content/uploads/2021/01/Digital-Survey-Case-Study-Hotel-MEP_V2.pdf.
ClearEdge3D. 2021b. “Case study: Chicago Federal Center Restoration—Save time by eliminating duplicate modeling steps with EdgeWise building.” Accessed February 20, 2022. https://www.clearedge3d.com/wp-content/uploads/2021/01/Ghafari-Chicago-Federal-Building-EWB_V2.pdf.
ClearEdge3D. 2021c. “Case study: University science building as-built revit model—EdgeWise building delivers a 55% workflow improvement for scan-to-revit modeling.” Accessed February 20, 2022. https://www.clearedge3d.com/wp-content/uploads/2021/01/3DIS-University-Science-Bldg-EWB_V2.pdf.
Czerniawski, T., and F. Leite. 2020. “Automated digital modeling of existing buildings: A review of visual object recognition methods.” Autom. Constr. 113: 103131. https://doi.org/10.1016/j.autcon.2020.103131.
Dadrasjavan, F., N. Zarrinpanjeh, A. Ameri, G. Engineering, and Q. Branch. “Automatic crack detection of road pavement based on aerial UAV imagery.” Preprint, submitted July 1, 2019. https://doi.org/10.20944/preprints201907.0009.v1.
D’Angelo, L., M. Hajdukiewicz, F. Seri, and M. M. Keane. 2022. “A novel BIM-based process workflow for building retrofit.” J. Build. Eng. 50: 104163. https://doi.org/10.1016/j.jobe.2022.104163.
DJI. 2022. “Drone solutions for architecture, engineering, and construction (AEC).” Accessed February 20, 2022. https://enterprise.dji.com/surveying/aec.
Dore, C., and M. Murphy. 2014. “Semi-automatic generation of as-built BIM façade geometry from laser and image data.” J. Inf. Technol. Constr. 19 (Jan): 20–46.
Durdyev, S., M. Ashour, S. Connelly, and A. Mahdiyar. 2022. “Barriers to the implementation of building information modelling (BIM) for facility management.” J. Build. Eng. 46: 103736. https://doi.org/10.1016/j.jobe.2021.103736.
Edmondson, V., J. Woodward, M. Lim, M. Kane, J. Martin, and I. Shyha. 2019. “Improved non-contact 3D field and processing techniques to achieve macrotexture characterisation of pavements.” Constr. Build. Mater. 227: 116693. https://doi.org/10.1016/j.conbuildmat.2019.116693.
Frohlich, B., E. Rodner, and J. Denzler. 2010. “A fast approach for pixelwise labeling of facade images.” In Proc., 20th Int. Conf. on Pattern Recognition, 3029–3032. New York: IEEE.
Hall, J. R. 2011. “High-rise building fires.” National Fire Protection Association. Accessed January 6, 2021. http://www.nfpa.org/assets/files/PDF/OS.HighRise.pdf.
Han, S., Y. Jiang, and Y. Bai. 2022. “Fast-PGMED: Fast and dense elevation determination for earthwork using drone and deep learning.” J. Constr. Eng. Manage. 148 (4): 04022008. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002256.
Higgins, S. 2022a. “Scan-to-BIM basics: Best practices for modeling.” Accessed February 19, 2022. https://www.navvis.com/blog/scan-to-bim-basics-best-practices-for-modeling.
Higgins, S. 2022b. “BIM through the building lifecycle.” Accessed February 19, 2022. https://www.navvis.com/blog/bim-through-the-building-lifecycle.
Higgins, S. 2022c. “As-designed, as-built, as-constructed, as-is—What’s the difference?” Accessed February 19, 2022. https://www.navvis.com/blog/as-designed-as-built-as-constructed-as-is-differences.
Hong, S., J. Jung, S. Kim, H. Cho, J. Lee, and J. Heo. 2015. “Semi-automated approach to indoor mapping for 3D as-built building information modeling.” Comput. Environ. Urban Syst. 51: 34–46. https://doi.org/10.1016/j.compenvurbsys.2015.01.005.
Hsieh, Y.-A., and Y. J. Tsai. 2020. “Machine learning for crack detection: Review and model performance comparison.” J. Comput. Civ. Eng. 34 (5): 04020038. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000918.
Huang, R., B. Yang, F. Liang, W. Dai, J. Li, M. Tian, and W. Xu. 2018. “A top-down strategy for buildings extraction from complex urban scenes using airborne LiDAR point clouds.” Infrared Phys. Technol. 92 (May): 203–218. https://doi.org/10.1016/j.infrared.2018.05.021.
Jacob-Loyola, N., F. Muñoz-La Rivera, R. F. Herrera, and E. Atencio. 2021. “Unmanned aerial vehicles (UAVs) for physical progress monitoring of construction.” Sensors 21 (12): 4227. https://doi.org/10.3390/s21124227.
Ji, A., X. Xue, Y. Wang, X. Luo, and W. Xue. 2020. “An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement.” Autom. Constr. 114 (Mar): 103176. https://doi.org/10.1016/j.autcon.2020.103176.
Jiang, Y. 2020. “Object detection via mesh model and deep learning.” Accessed February 22, 2022. https://www.yuhanjiang.com/research/FM/3DMesh.
Jiang, Y. 2021a. “As-built CAD drawing tool.” Accessed July 14, 2021. https://www.yuhanjiang.com/research/DT/CAD.
Jiang, Y. 2021b. “Dataset.” Accessed July 14, 2021. https://www.yuhanjiang.com/dataset.
Jiang, Y. 2022. “Pointcloud2Orthoimage.” Accessed February 22, 2022. https://www.yuhanjiang.com/research/FM/PC/P2I.
Jiang, Y., and Y. Bai. 2021. “Low–high orthoimage pairs-based 3D reconstruction for elevation determination using drone.” J. Constr. Eng. Manage. 147 (9): 04021097. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002067.
Jiang, Y., Y. Bai, and S. Han. 2020. “Determining ground elevations covered by vegetation on construction sites using drone-based orthoimage and convolutional neural network.” J. Comput. Civ. Eng. 34 (6): 04020049. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000930.
Jiang, Y., S. Han, and Y. Bai. 2021a. “Development of a pavement evaluation tool using aerial imagery and deep learning.” J. Transp. Eng. Part B: Pavements 147 (3): 04021027. https://doi.org/10.1061/JPEODX.0000282.
Jiang, Y., S. Han, and Y. Bai. 2021b. “Building and infrastructure defect detection and visualization using drone and deep learning technologies.” J. Perform. Constr. Facil 35 (6): 04021092. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001652.
Jiang, Y., S. Han, and Y. Bai. 2022a. “Construction site segmentation using drone-based ortho-image and convolutional encoder–decoder network model.” In Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics, edited by F. Jazizadeh, T. Shealy, and M. J. Garvin, 1096–1105. Reston, VA: ASCE.
Jiang, Y., S. Han, D. Li, Y. Bai, and M. Wang. 2022b. “Automatic concrete sidewalk deficiency detection and mapping with deep learning.” Expert Syst. Appl. 207 (Nov): 117980. https://doi.org/10.1016/j.eswa.2022.117980.
Jung, J., S. Hong, S. Jeong, S. Kim, H. Cho, S. Hong, and J. Heo. 2014. “Productive modeling for development of as-built BIM of existing indoor structures.” Autom. Constr. 42: 68–77. https://doi.org/10.1016/j.autcon.2014.02.021.
Jung, J., S. Hong, S. Yoon, J. Kim, and J. Heo. 2016. “Automated 3D wireframe modeling of indoor structures from point clouds using constrained least-squares adjustment for as-built BIM.” J. Comput. Civ. Eng. 30 (4): 04015074. https://doi.org/10.1061/(asce)cp.1943-5487.0000556.
Karachaliou, E., E. Georgiou, D. Psaltis, and E. Stylianidis. 2019. “UAV for mapping historic buildings: From 3D modelling to BIM.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-2/W9 (2/W9): 397–402. https://doi.org/10.5194/isprs-archives-XLII-2-W9-397-2019.
Karunaratne, S., and D. Dharmarathna. 2022. “A review of comprehensiveness, user-friendliness, and contribution for sustainable design of whole building environmental life cycle assessment software tools.” Build. Environ. 212: 108784. https://doi.org/10.1016/j.buildenv.2022.108784.
Kearney, S. P., N. C. Coops, S. Sethi, and G. B. Stenhouse. 2020. “Maintaining accurate, current, rural road network data: An extraction and updating routine using RapidEye, participatory GIS and deep learning.” Int. J. Appl. Earth Obs. Geoinf. 87 (Sep 2019): 102031. https://doi.org/10.1016/j.jag.2019.102031.
Kellner, J. R., et al. 2019. “New opportunities for forest remote sensing through ultra-high-density drone lidar.” Surv. Geophys. 40 (4): 959–977. https://doi.org/10.1007/s10712-019-09529-9.
Knyaz, V. A., O. Vygolov, V. V. Kniaz, Y. Vizilter, V. Gorbatsevich, T. Luhmann, and N. Conen. 2017. “Deep learning of convolutional auto-encoder for image matching and 3D object reconstruction in the infrared range.” In Proc., IEEE Int. Conf. on Computer Vision Workshops, 2155–2164. New York: IEEE.
Koo, B., R. Jung, and Y. Yu. 2021. “Automatic classification of wall and door BIM element subtypes using 3D geometric deep neural networks.” Adv. Eng. Inf. 47 (Nov): 101200. https://doi.org/10.1016/j.aei.2020.101200.
Kussul, N., M. Lavreniuk, S. Skakun, and A. Shelestov. 2017. “Deep learning classification of land cover and crop types using remote sensing data.” IEEE Geosci. Remote Sens. Lett. 14 (5): 778–782. https://doi.org/10.1109/LGRS.2017.2681128.
Lee, J., H. Son, C. Kim, and C. Kim. 2013. “Skeleton-based 3D reconstruction of as-built pipelines from laser-scan data.” Autom. Constr. 35: 199–207. https://doi.org/10.1016/j.autcon.2013.05.009.
Li, Z., C. Cheng, M.-P. Kwan, X. Tong, and S. Tian. 2019. “Identifying asphalt pavement distress using UAV LiDAR point cloud data and random forest classification.” ISPRS Int. J. Geo-Inf. 8 (1): 39. https://doi.org/10.3390/ijgi8010039.
Lippoldt, F. 2019. “Window detection in facades for aerial texture files of 3D CityGML models.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 11–19. Piscataway, NJ: IEEE.
Liu, T., and A. Abd-Elrahman. 2018. “Deep convolutional neural network training enrichment using multi-view object-based analysis of unmanned aerial systems imagery for wetlands classification.” ISPRS J. Photogramm. Remote Sens. 139: 154–170. https://doi.org/10.1016/j.isprsjprs.2018.03.006.
Liu, T., A. Abd-Elrahman, J. Morton, and V. L. Wilhelm. 2018. “Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system.” GISci. Remote Sens. 55 (2): 243–264. https://doi.org/10.1080/15481603.2018.1426091.
Liu, Z., Y. Cao, Y. Wang, and W. Wang. 2019. “Computer vision-based concrete crack detection using U-net fully convolutional networks.” Autom. Constr. 104 (Apr): 129–139. https://doi.org/10.1016/j.autcon.2019.04.005.
Luo, W., A. G. Schwing, and R. Urtasun. 2016. “Efficient deep learning for stereo matching.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 5695–5703. New York: IEEE.
Ma, J. W., T. Czerniawski, and F. Leite. 2020. “Semantic segmentation of point clouds of building interiors with deep learning: Augmenting training datasets with synthetic BIM-based point clouds.” Autom. Constr. 113 (Feb): 103144. https://doi.org/10.1016/j.autcon.2020.103144.
Macher, H., T. Landes, and P. Grussenmeyer. 2017. “From point clouds to building information models: 3D semi-automatic reconstruction of indoors of existing buildings.” Appl. Sci. 7 (10): 1030. https://doi.org/10.3390/app7101030.
Majidifard, H., Y. Adu-Gyamfi, and W. G. Buttlar. 2020. “Deep machine learning approach to develop a new asphalt pavement condition index.” Constr. Build. Mater. 247 (Feb): 118513. https://doi.org/10.1016/j.conbuildmat.2020.118513.
Maniat, M. 2019. Deep learning-based visual crack detection using Google Street View images. Memphis, TN: Univ. of Memphis.
Mohammadi, J. 2021. “Building facade inspection process: Administration and management matters.” Pract. Period. Struct. Des. Constr. 26 (3): 02521001. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000585.
Motalebi, M., A. Rashidi, and M. M. Nasiri. 2022. “Optimization and BIM-based lifecycle assessment integration for energy efficiency retrofit of buildings.” J. Build. Eng. 49: 104022. https://doi.org/10.1016/j.jobe.2022.104022.
OpenCV. 2020. “Contours in OpenCV.” Accessed November 9, 2020. https://docs.opencv.org/3.4/d3/d05/tutorial_py_table_of_contents_contours.html.
Park, J., J. Chen, and Y. K. Cho. 2020. “Point cloud information modeling (PCIM): An innovative framework for as-is information modeling of construction sites.” In Construction Research Congress, 1319–1326. Reston, VA: ASCE.
Park, J., and Y. K. Cho. 2022. “Point cloud information modeling: Deep learning-based automated information modeling framework for point cloud data.” J. Constr. Eng. Manage. 148 (2): 1–14. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002227.
Park, Y., and J.-M. Guldmann. 2019. “Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach.” Comput. Environ. Urban Syst. 75 (Jan): 76–89. https://doi.org/10.1016/j.compenvurbsys.2019.01.004.
Pérez, G., A. Escolà, J. R. Rosell-Polo, J. Coma, R. Arasanz, B. Marrero, L. F. Cabeza, and E. Gregorio. 2021. “3D characterization of a Boston Ivy double-skin green building facade using a LiDAR system.” Build. Environ. 206 (Sep): 108320. https://doi.org/10.1016/j.buildenv.2021.108320.
Perez-Perez, Y., M. Golparvar-Fard, and K. El-Rayes. 2020. “Convolutional neural network architecture for semantic labeling structural and mechanical elements.” In Construction Research Congress, edited by P. Tang, D. Grau, and M. El Asmar, 1336–1345. Reston, VA: ASCE.
Perez-Perez, Y., M. Golparvar-Fard, and K. El-Rayes. 2021. “Scan2BIM-NET: Deep learning method for segmentation of point clouds for scan-to-BIM.” J. Constr. Eng. Manage. 147 (9): 04021107. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002132.
Piaseckienė, G. 2022. “Dimensions of BIM in literature: Review and analysis.” Mokslas—Lietuvos Ateitis 14: 1–11. https://doi.org/10.3846/mla.2022.16071.
Pix4D. 2018. “Michigan Central Station.” Accessed July 31, 2020. https://cloud.pix4d.com/dataset/258513/files/inputs?shareToken=060367e6115f4185902cd33556a70e38.
Protopapadakis, E., A. Voulodimos, A. Doulamis, N. Doulamis, and T. Stathaki. 2019. “Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing.” Appl. Intell. 49 (7): 2793–2806. https://doi.org/10.1007/s10489-018-01396-y.
Resop, J. P., L. Lehmann, and W. C. Hession. 2019. “Drone laser scanning for modeling riverscape topography and vegetation: Comparison with traditional aerial lidar.” Drones 3 (2): 35. https://doi.org/10.3390/drones3020035.
Roberts, R., L. Inzerillo, and G. Di Mino. 2020. “Exploiting low-cost 3D imagery for the purposes of detecting and analyzing pavement distresses.” Infrastructures 5 (1): 6. https://doi.org/10.3390/infrastructures5010006.
Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional networks for biomedical image segmentation.” In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), edited by N. Navab, J. Hornegger, W. Wells, and A. Frangi, 234–241. Cham, Switzerland: Springer.
Seiler, A. 2013. “The station: Michigan central station thesis.” Accessed July 30, 2020. https://alliseseilerthesis.wordpress.com/entire-first-floor-over-view/.
Shelhamer, E., J. Long, and T. Darrell. 2017. “Fully convolutional networks for semantic segmentation.” IEEE Trans. Pattern Anal. Mach. Intell. 39 (4): 640–651. https://doi.org/10.1109/TPAMI.2016.2572683.
Shi, Z., and S. Ergan. 2020. “Towards point cloud and model-based urban façade inspection: Challenges in the urban façade inspection process.” In Construction Research Congress 2020: Safety, Workforce, and Education, edited by M. El Asmar, D. Grau, and P. Tang, 385–394. Reston, VA: ASCE.
Song, W., G. Jia, H. Zhu, D. Jia, and L. Gao. 2020. “Automated pavement crack damage detection using deep multiscale convolutional features.” J. Adv. Transp. 2020: 6412562. https://doi.org/10.1155/2020/6412562.
Takahashi, N., R. Wakutsu, T. Kato, T. Wakaizumi, T. Ooishi, and R. Matsuoka. 2017. “Experiment on UAV photogrammetry and terrestrial laser scanning for ICT-integrated construction.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-2/W6 (2W6): 371–377. https://doi.org/10.5194/isprs-archives-XLII-2-W6-371-2017.
Tang, P., D. Huber, B. Akinci, R. Lipman, and A. Lytle. 2010. “Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques.” Autom. Constr. 19 (7): 829–843. https://doi.org/10.1016/j.autcon.2010.06.007.
Wang, C., Y. K. Cho, and C. Kim. 2015. “Automatic BIM component extraction from point clouds of existing buildings for sustainability applications.” Autom. Constr. 56: 1–13. https://doi.org/10.1016/j.autcon.2015.04.001.
Xia, S., and R. Wang. 2019a. “Semiautomatic construction of 2-D façade footprints from mobile LiDAR data.” IEEE Trans. Geosci. Remote Sens. 57 (6): 4005–4020. https://doi.org/10.1109/TGRS.2018.2889335.
Xia, S., and R. Wang. 2019b. “Façade separation in ground-based LiDAR point clouds based on edges and windows.” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12 (3): 1041–1052. https://doi.org/10.1109/JSTARS.2019.2897987.
Xu, Y., X. Shen, S. Lim, and X. Li. 2021. “Three-dimensional object detection with deep neural networks for automatic as-built reconstruction.” J. Constr. Eng. Manage. 147 (9): 04021098. https://doi.org/10.1061/(asce)co.1943-7862.0002003.
Yang, L., J. C. P. Cheng, and Q. Wang. 2020. “Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data.” Autom. Constr. 112 (Jan): 103037. https://doi.org/10.1016/j.autcon.2019.103037.
Zhang, K., Y. Zhang, and H.-D. Cheng. 2021a. “CrackGAN: Pavement crack detection using partially accurate ground truths based on generative adversarial learning.” IEEE Trans. Intell. Transp. Syst. 22 (2): 1306–1319. https://doi.org/10.1109/tits.2020.2990703.
Zhang, Y., W. Yang, X. Liu, Y. Wan, X. Zhu, and Y. Tan. 2021b. “Unsupervised building instance segmentation of airborne LiDAR point clouds for parallel reconstruction analysis.” Remote Sens. 13 (6): 1136. https://doi.org/10.3390/rs13061136.
Zhao, H., J. Shi, X. Qi, X. Wang, and J. Jia. 2017. “Pyramid scene parsing network.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 6230–6239. New York: IEEE.
Zhi, X. 2019. “Implementation of deep learning framework—Unet, using Keras.” Accessed July 1, 2020. https://github.com/zhixuhao/unet.
Zhou, S., and W. Song. 2020a. “Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection.” Autom. Constr. 114 (Oct): 103171. https://doi.org/10.1016/j.autcon.2020.103171.
Zhou, S., and W. Song. 2020b. “Robust image-based surface crack detection using range data.” J. Comput. Civ. Eng. 34 (2): 04019054. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000873.
Zou, Q., Z. Zhang, Q. Li, X. Qi, Q. Wang, and S. Wang. 2019. “Deepcrack: Learning hierarchical convolutional features for crack detection.” IEEE Trans. Image Process. 28 (3): 1498–1512. https://doi.org/10.1109/TIP.2018.2878966.
Information & Authors
Information
Published In
Journal of Architectural Engineering
Volume 28 • Issue 4 • December 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jul 23, 2021
Accepted: Jun 15, 2022
Published online: Sep 1, 2022
Published in print: Dec 1, 2022
Discussion open until: Feb 1, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Buildings
- Computer programming
- Computer vision and image processing
- Computing in civil engineering
- Engineering fundamentals
- Geomatics
- High-rise buildings
- Historic buildings
- History and Heritage
- Methodology (by type)
- Models (by type)
- Photogrammetry
- Practice and Profession
- Structural engineering
- Structures (by type)
- Surveying methods
- Three-dimensional models
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Sisi Han, Yuhan Jiang, Yilei Huang, Mingzhu Wang, Yong Bai, Andrea Spool-White, Scan2Drawing: Use of Deep Learning for As-Built Model Landscape Architecture, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-13077, 149, 5, (2023).
- Yuhan Jiang, Yilei Huang, Jingkuang Liu, Dapeng Li, Shuiyuan Li, Weijing Nie, In-Hun Chung, Automatic Volume Calculation and Mapping of Construction and Demolition Debris Using Drones, Deep Learning, and GIS, Drones, 10.3390/drones6100279, 6, 10, (279), (2022).