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

Paper


Tarsha Kurdi, Fayez, Gharineiat, Zahra, Campbell, Glenn, Dey, Emon Kumar and Awrangjeb, Mohammad. 2021. "Full series algorithm of automatic building extraction and modelling from LiDAR data." Zhou, Jun, Salvado, Olivier, Sohel, Ferdous, Borges, Paulo and Wang, Shilin (ed.) 2021 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2021). Gold Coast, Australia 29 Nov - 01 Dec 2021 United States.
Paper/Presentation Title

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

Presentation TypePaper
AuthorsTarsha Kurdi, Fayez (Author), Gharineiat, Zahra (Author), Campbell, Glenn (Author), Dey, Emon Kumar (Author) and Awrangjeb, Mohammad (Author)
EditorsZhou, Jun, Salvado, Olivier, Sohel, Ferdous, Borges, Paulo and Wang, Shilin
Journal or Proceedings TitleProceedings of the 2021 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2021)
ERA Conference ID42717
Number of Pages8
Year2021
Place of PublicationUnited States
ISBN9781665417099
Web Address (URL) of Paperhttp://dicta2021.dictaconference.org/index.html
Conference/Event2021 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2021)
Digital Image Computing Techniques and Applications
Event Details
Digital Image Computing Techniques and Applications
DICTA
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Event Details
2021 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2021)
Parent
International Conference on Digital Image Computing: Techniques and Applications
Event Date
29 Nov 2021 to end of 01 Dec 2021
Event Location
Gold Coast, Australia
Abstract

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.

KeywordsLiDAR, classification, modelling, filtering, segmentation
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
461199. Machine learning not elsewhere classified
Public Notes

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Byline AffiliationsSchool of Civil Engineering and Surveying
Griffith University
Institution of OriginUniversity of Southern Queensland
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