Fast-PGMED: Fast and Dense Elevation Determination for Earthwork Using Drone and Deep Learning

Han, Sisi; Jiang, Yuhan; Bai, Yong

doi:10.1061/(asce)co.1943-7862.0002256

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Since the use of UAS showed satisfactory results both in terms of data collection time and 3D reconstruction, the UAS imagery combined with SfM photogrammetry can be potentially used to: (i) develop a geospatial database of the coastal structures on the Portuguese coast; (ii) to increase the temporal resolution of data collection and explore the potentialities of simultaneous localization additional mapping (SLAM) to improve the efficiency of data processing [56,57]; (iii) improve the effectiveness of inspection; (iv) monitor the evolution of likely damaged areas; (v) create a framework to automatically segment the armour units for analysing and quantifying the damage areas [58]; (vi) provide an effective tool to the decision makers and coastal management entities.…”

Section: Discussionmentioning

confidence: 99%

On the 3D Reconstruction of Coastal Structures by Unmanned Aerial Systems with Onboard Global Navigation Satellite System and Real-Time Kinematics and Terrestrial Laser Scanning

Gonçalves

Pérez-Alvávez

et al. 2022

Remote Sensing

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A wide variety of hard structures protect coastal activities and communities from the action of tides and waves worldwide. It is fundamental to monitor the integrity of coastal structures, as interventions and repairs may be needed in case of damages. This work compares the effectiveness of an Unmanned Aerial System (UAS) and a Terrestrial Laser Scanner (TLS) to reproduce the 3D geometry of a rocky groin. The Structure-from-Motion (SfM) photogrammetry technique applied on drone images generated a 3D point cloud and a Digital Surface Model (DSM) without data gaps. Even though the TLS returned a 3D point cloud four times denser than the drone one, the TLS returned a DSM which was not representing about 16% of the groin (data gaps). This was due to the occlusions encountered by the low-lying scans determined by the displaced rocks composing the groin. Given also that the survey by UAS was about eight time faster than the TLS, the SFM-MV applied on UAS images was the most suitable technique to reconstruct the rocky groin. The UAS remote sensing technique can be considered a valid alternative to monitor all types of coastal structures, to improve the inspection of likely damages, and to support coastal structure management.

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Section: Discussionmentioning

confidence: 99%

On the 3D Reconstruction of Coastal Structures by Unmanned Aerial Systems with Onboard Global Navigation Satellite System and Real-Time Kinematics and Terrestrial Laser Scanning

Gonçalves

Pérez-Alvávez

et al. 2022

Remote Sensing

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“…The main disadvantage of laser scanning is that the device is costly (c. €30,000); accuracy is 2-3 mm at a scanning distance of about 70 m, and a reliable unit weighs at least 7-8 kg (Galanakis et al 2021). One common method employs a sensor attached to an unmanned aerial vehicle (UAV) (Di Leo et al 2011), which enables monitoring of large areas (Zhu et al 2020;Jensen et al 2020;Han et al 2022). The choice of an appropriate technique depends on site dimensions, morphology, and topography, as well as accessibility, visibility, safety, and careful cost-benefit analysis (Tucci et al 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Niskanen

Immonen

Hallman

et al. 2023

J. Transp. Eng., Part B: Pavements

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Construction materials and related management, handling, and storage provisions account for a large part of road construction expenses. For that reason, improved material flow monitoring techniques can achieve significant cost and time savings, as well as better quality control. The study assessed the performance of a solid-state pulsed time-of-flight laser lidar profilometer in measuring the volume of soil stockpiles and road layer thicknesses. The 3D (X, Y, Z, and intensity) image calculation was based on the analysis of multiple combined point clouds measured with an excavator-integrated profilometer. Error analysis confirmed the accuracy of road layer thickness estimation within one centimetre and an error level of approximately 3% when measuring soil stockpile volumes. In combination with a theoretical model of the superstructure, this 3D measurement technique can help contractors and supervisors to ensure road quality.

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“…Shape and position control in concrete casting and precast installation requires finding accurate object/segment boundaries [7]. Volume calculations in the construction and demolition (C&D) phases can be automated from laser scanner, UAV, or satellite syn-thetic aperture radar (SAR) footage with the help of semantic segmentation [8,9]. Reverse engineering digital twins of existing buildings (as built and scan to BIM) requires assigning element classes to scan data to reconstruct element geometries [10,11].…”

Section: Introductionmentioning

confidence: 99%

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Rauch

Braml

2023

Applied Sciences

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Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. Point cloud semantic segmentation (PCSS) enables the automatic extraction of semantic information from 3D point cloud data, which makes it a desirable task for construction-related applications as well. Yet, only a limited number of publications have applied deep-learning-based methods to address point cloud understanding for civil engineering problems, and there is still a lack of comprehensive reviews and evaluations of PCSS methods tailored to such use cases. This paper aims to address this gap by providing a survey of recent advances in deep-learning-based PCSS methods and relating them to the challenges of the construction industry. We introduce its significance for the industry and provide a comprehensive look-up table of publicly available datasets for point cloud understanding, with evaluations based on data scene type, sensors, and point features. We address the problem of class imbalance in 3D data for machine learning, provide a compendium of commonly used evaluation metrics for PCSS, and summarize the most significant deep learning methods developed for PCSS. Finally, we discuss the advantages and disadvantages of the methods for specific industry challenges. Our contribution, to the best of our knowledge, is the first survey paper that comprehensively covers deep-learning-based methods for semantic segmentation tasks tailored to construction applications. This paper serves as a useful reference for prospective research and practitioners seeking to develop more accurate and efficient PCSS methods.

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Fast-PGMED: Fast and Dense Elevation Determination for Earthwork Using Drone and Deep Learning

Cited by 8 publications

References 38 publications

On the 3D Reconstruction of Coastal Structures by Unmanned Aerial Systems with Onboard Global Navigation Satellite System and Real-Time Kinematics and Terrestrial Laser Scanning

On the 3D Reconstruction of Coastal Structures by Unmanned Aerial Systems with Onboard Global Navigation Satellite System and Real-Time Kinematics and Terrestrial Laser Scanning

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

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