Han, S; Jiang, Y; Huang, Y; Wang, M; Bai, Y and Spool-White, A (2023) Scan2drawing: Use of deep learning for as-built model landscape architecture. Journal of Construction Engineering and Management, 149(5): 04023027, ISSN 0733-9364
Abstract
This paper presents an innovative and fully automatic solution of generating as-built computer-aided design (CAD) drawings for landscape architecture (LA) with three dimensional (3D) reality data scanned via drone, camera, and LiDAR. To start with the full pipeline, 2D feature images of ortho-image and elevation-map are converted from the reality data. A deep learning-based light convolutional encoder-decoder was developed, and compared with U-Net (a binary segmentation model), for image pixelwise segmentation to realize automatic site surface classification, object detection, and ground control point identification. Then, the proposed elevation clustering and segmentation algorithms can automatically extract contours for each instance from each surface or object category. Experimental results showed that the developed light model achieved comparable results with U-Net in landing pad segmentation with average intersection over union (IoU) of 0.900 versus 0.969. With the proposed data augmentation strategy, the light model had a testing pixel accuracy of 0.9764 and mean IoU of 0.8922 in the six-class segmentation testing task. Additionally, for surfaces with continuous elevation changes (i.e., ground), the developed algorithm created contours only have an average elevation difference of 1.68 cm compared to dense point clouds using drones and image-based reality data. For objects with discrete elevation changes (i.e., stair treads), the generated contours accurately represent objects' elevations with zero difference using light detection and ranging (LiDAR) data. The contribution of this research is to develop algorithms that automatically transfer the scanned LA scenes to contours with real-world coordinates to create as-built computer-aided design (CAD) drawings, which can further assist building information modeling (BIM) model creation and inspect the scanned LA scenes with augmented reality. The optimized parameters for the developed algorithms are analyzed and recommended for future applications.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 3D reality data; as-built; augmented reality; building information modeling; clustering; computer-aided design drawing; deep learning; LIDAR; object extraction; segmentation |
| Index terms: | testing, clustering, deep learning, pipeline, building information modelling, object detection, computer aided design, augmented reality, as-built model, accuracy, point cloud, drawing, strategy, drone |
| Subjects: | information systems, infrastructure and transport systems, management, professional development, digital design, automation and robotics, computer vision, technical documentation, visualization, artificial intelligence, data science, professional practice, computational design |
| Topics: | Digital Applications, Design Practice, Business Strategy, Engineering Principles, Information Management |
| Descriptive scope: | 2 PC |
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