A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree

Jifroudi, HM; Mansor, SB; Pradhan, B; Halin, AA; Ahmad, N; Abdullah, AFB

A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree

Jifroudi, HM Mansor, SB Pradhan, B

Halin, AA Ahmad, N Abdullah, AFB

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Measurement: Journal of the International Measurement Confederation, 2022, 192
Issue Date:: 2022-03-31

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Field	Value	Language
dc.contributor.author	Jifroudi, HM
dc.contributor.author	Mansor, SB
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Halin, AA
dc.contributor.author	Ahmad, N
dc.contributor.author	Abdullah, AFB
dc.date.accessioned	2023-03-09T23:28:33Z
dc.date.available	2023-03-09T23:28:33Z
dc.date.issued	2022-03-31
dc.identifier.citation	Measurement: Journal of the International Measurement Confederation, 2022, 192
dc.identifier.issn	0263-2241
dc.identifier.issn	1873-412X
dc.identifier.uri	http://hdl.handle.net/10453/166923
dc.description.abstract	Buildings are among the most important elements in the urban structure that can affect urban planning. Therefore, it is important to create the footprint of the buildings, especially in developing cities, which is highly time-consuming and costly. Although LiDAR technology has already been used for this purpose, the need to process voluminous amounts of noisy data and make building footprint extraction in accurate. In this study, we propose a step-by-step analysis of LiDAR data using a rule-based algorithm called DB-creator in order to automatically create building footprints. DB-creator was specifically chose as it does not require external data or region information to construct the footprints. The constructed footprints based from the algorithm was compared with manually created ground truth building footprints to assess accuracy. From experimental results, RMSE for urban and rural areas were ± 0.62 m and ± 0.28 m, respectively, which is highly accurate considering LiDAR's a 0.5 m surveying distance between two points and 0.6 m distance between rows. Moreover, the kappa coefficient were 0.948 and 0.958 for the urban and rural areas, respectively (which are confirmed by T values of 150.204 and 255.553 at p ≤ 0.01 for the urban and rural areas, respectively). The Standard Errors respectively obtained for urban and rural areas were 0.001 and 0.002, reflecting slight internal variations between the built footprint maps. This also highlights the certainty of the kappa coefficient, indicating that extraction of building footprints is highly accurate.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Measurement: Journal of the International Measurement Confederation
dc.relation.isbasedon	10.1016/j.measurement.2022.110781
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0913 Mechanical Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree
dc.type	Journal Article
utslib.citation.volume	192
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-09T23:28:30Z
pubs.publication-status	Published
pubs.volume	192

Abstract:

Buildings are among the most important elements in the urban structure that can affect urban planning. Therefore, it is important to create the footprint of the buildings, especially in developing cities, which is highly time-consuming and costly. Although LiDAR technology has already been used for this purpose, the need to process voluminous amounts of noisy data and make building footprint extraction in accurate. In this study, we propose a step-by-step analysis of LiDAR data using a rule-based algorithm called DB-creator in order to automatically create building footprints. DB-creator was specifically chose as it does not require external data or region information to construct the footprints. The constructed footprints based from the algorithm was compared with manually created ground truth building footprints to assess accuracy. From experimental results, RMSE for urban and rural areas were ± 0.62 m and ± 0.28 m, respectively, which is highly accurate considering LiDAR's a 0.5 m surveying distance between two points and 0.6 m distance between rows. Moreover, the kappa coefficient were 0.948 and 0.958 for the urban and rural areas, respectively (which are confirmed by T values of 150.204 and 255.553 at p ≤ 0.01 for the urban and rural areas, respectively). The Standard Errors respectively obtained for urban and rural areas were 0.001 and 0.002, reflecting slight internal variations between the built footprint maps. This also highlights the certainty of the kappa coefficient, indicating that extraction of building footprints is highly accurate.

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http://hdl.handle.net/10453/166923