Deep learning (DL) on point clouds holds significant potential in the construction industry,
yet no comprehensive review has thoroughly summarized its applications and shortcomings …

Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

Managing, protecting, and the evolutionary development of historical landscapes require
robust frameworks and processes for forming datasets and advanced decision support tools …

Abstract The use of Unmanned Aerial Vehicles (UAVs) for bridge inspection has gained
popularity recently; however, accurately localising the UAV in GPS-denied areas is still …

The cost of obtaining large volumes of bridge data with technologies like laser scanners
hinders the training of deep learning models. To address this, this paper introduces a new …

Falls from scaffolds cause the majority of accidents and fatalities at construction sites. A
deep learning-based 3D reconstruction technology could provide a solution to prevent such …

Semantic segmentation of bridge Point Cloud Data (PCD) is an intermediate process
required for the tasks such as deformation detection and digital twin. However, existing …

The increasing availability of point clouds has led to intensive research into automating point
cloud processing using machine learning. While supervised systems require large and …

Point cloud semantic segmentation for mechanical, electrical, and plumbing (MEP) systems
is crucial for establishing MEP systems digital twins. Deep learning has shown promise in …

Focusing on learning‐based semantic segmentation (SS) methods for bridge point cloud
data (PCD), this study proposes a structure‐oriented concept (SOC) with training focused on …