Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach

Park, Yujin; Guldmann, Jean‐Michel

doi:10.1016/j.compenvurbsys.2019.01.004

Cited by 105 publications

(47 citation statements)

References 35 publications

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“…The method of constructing the CDSM for the analysis consisting of meshing and extruding the first return of the object is simple, fast, user-independent, and does not require any additional data but can generate geometry errors and inaccuracies especially in the case of buildings and tree objects. The QLA360 method can be directly applied for already existing 3D city models with different Level of Detail (LOD), which can improve analysis results due to the better buildings geometry representation [53,54], and virtual environments made with game engines [55]. The reconstruction of individual trees is possible but requires a very high density LiDAR collected using UAV platforms [56,57] or Terrestrial Laser Scanning [58,59] or manually modeling vegetation in 3D modeling software.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Wróżyński

Pyszny²,

Sojka

2020

Remote Sensing

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The study presents a new method for quantitative landscape assessment. The method uses LiDAR data and combines the potential of GIS (ArcGIS) and 3D graphics software (Blender). The developed method allows one to create Classified Digital Surface Models (CDSM), which are then used to create 360° panoramic images from the point of view of the observer. In order to quantify the landscape, 360° panoramic images were transformed to the Interrupted Sinusoidal Projection using G.Projector software. A quantitative landscape assessment is carried out automatically with the following landscape classes: ground, low, medium, and high vegetation, buildings, water, and sky according to the LiDAR 1.2 standard. The results of the analysis are presented quantitatively—the percentage distribution of landscape classes in the 360° field of view. In order to fully describe the landscape around the observer, graphs of little planets have been proposed to interpret the obtained results. The usefulness of the developed methodology, together with examples of its application and the way of presenting the results, is described. The proposed Quantitative Landscape Assessment method (QLA360) allows quantitative landscape assessment to be performed in the 360° field of view without the need to carry out field surveys. The QLA360 uses LiDAR American Society of Photogrammetry and Remote Sensing (ASPRS) classification standards, which allows one to avoid differences resulting from the use of different algorithms for classifying images in semantic segmentation. The most important advantages of the method are as follows: observer-independent, 360° field of view which simulates human perspective, automatic operation, scalability, and easy presentation and interpretation of results.

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Section: Discussionmentioning

confidence: 99%

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Wróżyński

Pyszny²,

Sojka

2020

Remote Sensing

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“…Nevertheless, OSM data are still useful as a map backdrop or for positional applications, such as routing and size calculations. With OSM data as backdrop and with the availability of building footprint vector data and LiDAR (Light Detection and Ranging) point clouds, the generation of large-scale 3D city models at low cost is increasing [43,78] (see Figure 8). Without active contributors, a geospatial dataset will quickly degenerate.…”

Section: Collaboratively Contributed Open Geospatial Datamentioning

confidence: 99%

Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future

Coetzee

Ivánová

Mitášová

et al. 2020

IJGI

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All over the world, organizations are increasingly considering the adoption of open source software and open data. In the geospatial domain, this is no different, and the last few decades have seen significant advances in this regard. We review the current state of open source geospatial software, focusing on the Open Source Geospatial Foundation (OSGeo) software ecosystem and its communities, as well as three kinds of open geospatial data (collaboratively contributed, authoritative and scientific). The current state confirms that openness has changed the way in which geospatial data are collected, processed, analyzed, and visualized. A perspective on future developments, informed by responses from professionals in key organizations in the global geospatial community, suggests that open source geospatial software and open geospatial data are likely to have an even more profound impact in the future.

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“…Random forest (RF) consists of multiple decision trees and allows them to select the most popular class (Breiman 2001). In the study of Park and Guldmann (2019), the RF algorithm was employed to classify LiDAR points into four different categories of building elements, reaching a high accuracy (96.5%). RF classifier is more robust to the quality of training examples and overfitting than other ML classifiers (Belgiu and Drăguţ 2016).…”

Section: As-built Bim Model Generationmentioning

confidence: 99%

Building a Next Generation AI Platform for AEC: A Review and Research Challenges

Wang¹,

He²,

Yu³

et al. 2020

CIB W78 Proceedings

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Transformation of the AEC industry has been found to face significant and persistent hurdles both internal and external to the industry, such as industrial fragmentation, difficulty in talent recruiting, inadequate collaboration and knowledge transfer. Meanwhile, advances and maturity in digital technologies recent years offer tremendous potential to tackle these challenges. Examples include Artificial Intelligence (AI) and deep learning, computer vision, big data analytics, Building Information Models (BIM), 3D scanning, Augmented Reality (AR), Unmanned Aerial Vehicles (UAVs), autonomous driving, and blockchain. We envision that the next generation platform for the AEC industry shall integrate this diverse list of digital technologies. In this paper, we review the stateof-the-art in related literature aiming at creating such an integrated platform. Our review is organized into the following four themes, and for each of them, we also identify gaps and challenges. 1. AI-based BIM model generation: this topic addresses how to make BIM models more ubiquitous, which includes the automatic generation of As-planned BIM model from 2D drawings and the automatic generation of As-built BIM model from site images/videos and point clouds. 2. Blockchain-based collaboration: this theme explores the possibilities of applying Blockchain for AEC to improve various essential aspects such as trust in multi-party collaboration and contract enforcement. 3. AR-based visualization: this topic studies the AR-based visualization for construction management, which aims to increase the efficiency of information transmission. 4. AI-based Construction management: this area reviews the application of AI in progress monitoring, safety management, quality management, and contract management. Based on the above analysis, we conclude by outlining potential topics of a research framework for creating a next generation AI platform for AEC.

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Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach

Cited by 105 publications

References 35 publications

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future

Building a Next Generation AI Platform for AEC: A Review and Research Challenges

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