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Virtual Cities: 3D Urban Modeling from Low Resolution LiDAR Data

Authors:

Anas AlfarisAuthors Info & Claims

SIGSPATIAL '17: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Article No.: 86, Pages 1 - 4

https://doi.org/10.1145/3139958.3140025

Published: 07 November 2017 Publication History

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Abstract

City-scale 3D models represent an important component in the analysis of urban design, and the analysis of urban systems such as energy and transportation systems. Using manual methods to build large-scale 3D models is a time-consuming and an expensive process. Thus, advancing 3D modeling automation literature can greatly contribute to and complement smart-city initiatives. In this paper, we demonstratean efficient and fully-automatic 3D modeling pipeline that utilizes low resolution noisy LiDAR dataset to create city-scale 3D model.

References

[1]

Hossein Arefi. 2009. From LiDAR point clouds to 3D building models. Ph.D. Dissertation.

Google Scholar

[2]

Liang Cheng, Jianya Gong, Manchun Li, and Yongxue Liu. 2011. 3D building model reconstruction from multi-view aerial imagery and lidar data. Photogrammetric Engineering & Remote Sensing 77, 2 (2011), 125--139.

Crossref

Google Scholar

[3]

David H Douglas and Thomas K Peucker. 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization 10, 2 (1973), 112--122.

Google Scholar

[4]

Herbert Edelsbrunner, David Kirkpatrick, and Raimund Seidel. 1983. On the shape of a set of points in the plane. IEEE Transactions on information theory 29, 4 (1983), 551--559.

Digital Library

Google Scholar

[5]

Yuxiang He. 2015. Automated 3D building modelling from airborne LiDAR data. Ph.D. Dissertation. UNIVERSITY OF MELBOURNE.

Google Scholar

[6]

Bogdan C Matei, Harpreet S Sawhney, Supun Samarasekera, Janet Kim, and Rakesh Kumar. 2008. Building segmentation for densely built urban regions using aerial lidar data. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 1--8.

Crossref

Google Scholar

[7]

Charalambos Poullis and Suya You. 2009. Automatic reconstruction of cities from remote sensor data. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2775--2782.

Crossref

Google Scholar

[8]

P Redweik, C Catita, and M Brito. 2013. Solar energy potential on roofs and facades in an urban landscape. Solar Energy 97 (2013), 332--341.

Crossref

Google Scholar

[9]

Christoph Reinhart, Timur Dogan, J Alstan Jakubiec, Tarek Rakha, and Andrew Sang. 2013. Umi-an urban simulation environment for building energy use, day-lighting and walkability. In 13th Conference of International Building Performance Simulation Association, Chambery, France.

Google Scholar

[10]

Bernard W Silverman. 1986. Density estimation for statistics and data analysis. Vol. 26. CRC press.

Google Scholar

[11]

Gunho Sohn and Ian Dowman. 2007. Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction. {ISPRS} Journal of Photogrammetry and Remote Sensing 62, 1 (2007), 43--63.

Google Scholar

[12]

Shaohui Sun. 2013. Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds. (2013).

Google Scholar

Cited By

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Ogawa YSato GSekimoto Y(2024)Geometric-based approach for linking various building measurement data to a 3D city modelPLOS ONE10.1371/journal.pone.029644519:1(e0296445)Online publication date: 5-Jan-2024
https://doi.org/10.1371/journal.pone.0296445
Brooks CIsci SKanza YKlosowski JWoods R(2023)ACM SIGSPATIAL GISCUP 2022 Workshop Report: Extracting Building Footprints from LiDAR Point Clouds Seattle, Washington, USA, November 1, 2022SIGSPATIAL Special10.1145/3632268.363228514:1(51-55)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1145/3632268.3632285
Xu XRenz MSarwat M(2022)An unsupervised building footprints delineation approach for large-scale LiDAR point cloudsProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3565986(1-4)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3565986
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Virtual Cities: 3D Urban Modeling from Low Resolution LiDAR Data

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Published In

SIGSPATIAL '17: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2017

677 pages

ISBN:9781450354905

DOI:10.1145/3139958

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2017

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SIGSPATIAL

SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 7 - 10, 2017

CA, Redondo Beach, USA

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SIGSPATIAL '17 Paper Acceptance Rate 39 of 193 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

View all

Ogawa YSato GSekimoto Y(2024)Geometric-based approach for linking various building measurement data to a 3D city modelPLOS ONE10.1371/journal.pone.029644519:1(e0296445)Online publication date: 5-Jan-2024
https://doi.org/10.1371/journal.pone.0296445
Brooks CIsci SKanza YKlosowski JWoods R(2023)ACM SIGSPATIAL GISCUP 2022 Workshop Report: Extracting Building Footprints from LiDAR Point Clouds Seattle, Washington, USA, November 1, 2022SIGSPATIAL Special10.1145/3632268.363228514:1(51-55)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1145/3632268.3632285
Xu XRenz MSarwat M(2022)An unsupervised building footprints delineation approach for large-scale LiDAR point cloudsProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3565986(1-4)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3565986
Chen WWang ZLi ZXie YJia XLi ARenz MSarwat M(2022)Deep semantic segmentation for building detection using knowledge-informed features from LiDAR point cloudsProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3565985(1-4)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3565985
Song HJung JRenz MSarwat M(2022)Challenges in building extraction from airborne LiDAR dataProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3565983(1-4)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3565983
Namouchi SFarah I(2021)Graph-Based Classification and Urban Modeling of Laser Scanning and Imagery: Toward 3D Smart Web ServicesRemote Sensing10.3390/rs1401011414:1(114)Online publication date: 28-Dec-2021
https://doi.org/10.3390/rs14010114
Seto TSekimoto YAsahi KEndo T(2020)Constructing a digital city on a web-3D platformProceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities10.1145/3423455.3430316(1-9)Online publication date: 3-Nov-2020
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Namouchi SVallet BFarah IIsmail H(2019)Piecewise Horizontal 3D Roof Reconstruction from Aerial LidarIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS.2019.8898650(8992-8995)Online publication date: Jul-2019
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Jones MHippolite NPatel RPeters SSingh PChen CWunsche B(2018)Towards a Graph-Based Approach for Mesh Healing for Blocky Objects with Self-Similarities2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ.2018.8634737(1-6)Online publication date: Nov-2018
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