Full series algorithm of automatic building extraction and modelling from LiDAR data

Tarsha Kurdi, Fayez and Gharineiat, Zahra ORCID: https://orcid.org/0000-0003-0913-151X and Campbell, Glenn ORCID: https://orcid.org/0000-0002-4249-2512 and Dey, Emon Kumar and Awrangjeb, Mohammad (2021) Full series algorithm of automatic building extraction and modelling from LiDAR data. In: 2021 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2021), 29 Nov - 1 Dec 2021, Gold Coast, Australia.


This paper suggests an algorithm that automatically links the automatic building classification and modelling algorithms. To make this connection, the suggested algorithm applies two filters to the building classification results that enable processing of the failed cases of the classification algorithm. In this context, it filters the noisy terrain class and analyses the remaining points to detect missing buildings. Moreover, it filters the detected building to eliminate all undesirable points such as those associated with trees overhanging the building roof, the surrounding terrain and the façade points. In the modelling algorithm, the error map matrix is analysed to recognize the failed cases of the building modelling algorithm with these buildings being modelled with flat roofs. Finally, the region growing algorithm is applied on the building mask to detect each building and pass it to the modelling algorithm. The accuracy analysis of the classification and modelling algorithm within the global algorithm shows it to be highly effective. Hence, the total error of the building classification algorithm is 0.01% and only one building in the sample dataset is rejected by the modelling algorithm and even that is modelled, but with a flat roof. Most of the buildings have Segmentation Accuracy and Quality factor less than 5% (error less than 5%) which means that the resulting evaluation is excellent.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Current - Institute for Advanced Engineering and Space Sciences - Centre for Future Materials (1 Jan 2017 -)
Date Deposited: 03 Dec 2021 00:31
Last Modified: 21 Jan 2022 02:19
Uncontrolled Keywords: LiDAR, classification, modelling, filtering, segmentation
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
Fields of Research (2020): 40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
E Expanding Knowledge > 97 Expanding Knowledge > 970112 Expanding Knowledge in Built Environment and Design
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280104 Expanding knowledge in built environment and design
URI: http://eprints.usq.edu.au/id/eprint/43699

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