Automatic Building Boundary Extraction from Airborne LiDAR Data

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André Caceres Carrilho
Renato César dos Santos
Guilherme Gomes Pessoa
Mauricio Galo

Abstract

This paper aims to evaluate the quality of the automatic extraction of building contours from airborne LiDAR data in order to verify if the errors obtained in the building areas are compatible with the criteria established by the Brazilian Law n° 10.406, January 10th, 2002. Two approaches were compared: one present in LAStools software (lasboundary) and the other being a variant of the α-shape algorithm, whose parameter α is estimated adaptively for each building. In addition, both approaches were applied to two datasets, one with an average point density of 5.8 points/m² and the other with 12.5 points/m². In this work three quality parameters were computed: F-score, relative error in area, and PoLiS metric. The assessment of the results indicates that most of the results met the criteria established by the above-mentioned law. The variant of α-shape algorithm seems to be better than the lasboundary, indicating that the dynamic approach to determine the parameter α contributes to the quality, especially on complex buildings. Finally, the use of the point cloud with higher average point density results in higher accuracy in the building boundary extraction.

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How to Cite
CARRILHO, A. C.; DOS SANTOS, R. C.; PESSOA, G. G.; GALO, M. Automatic Building Boundary Extraction from Airborne LiDAR Data. Brazilian Journal of Cartography, [S. l.], v. 71, n. 3, p. 832–855, 2019. DOI: 10.14393/rbcv71n3-46515. Disponível em: https://seer.ufu.br/index.php/revistabrasileiracartografia/article/view/46515. Acesso em: 22 nov. 2024.
Section
Original Articles

References

ALBERS, B.; KADA, M.; WICHMANN, A. Automatic extraction and regularization of building outlines from airborne LiDAR point clouds. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic, v. XLI-B3, p. 555-560, 2016.

AVBELJ, J.; M

AWRANGJEB, M. Using point cloud data to identify, trace, and regularize the outlines of buildings. International Journal of Remote Sensing, v. 37, n. 3, p. 551-579, 2016.

BRASIL. Lei nº 10.406, de 10 de janeiro de 2002. Institui o Código Civil. Brasilia, 2002. Disponível em: . Acesso em: 12 jul. 2018.

CARRILHO, A. C.; GALO, M. Extraction of building roof planes with stratified random sample consensus. The Photogrammetric Record, v. 33, p. 363-380, 2018.

CARRILHO, A. C.; GALO, M.; SANTOS, R. C.; PESSOA, G. G. Avaliação da extração automática de edificações a partir de dados lidar aerotransportado. In: Anais do COBRAC 2018, Florianópolis

CENTENO, J. A. S.; DAROS, R.; GARGON, J. P. Extração de DTM e detecção de construções em áreas urbanas usando LiDAR. In: Anais do COBRAC 2016, Florianópolis

DAL POZ, A.; HABIB, A. F.; MARCATO, V. J.; CORREIA, L. S. Uso de dados fotogramétricos no refinamento geométrico de contornos de telhados de edifícios extraídos de dados LASER. Boletim de Ciências Geodésicas, v. 15, n. 3, p. 594-614, 2009.

EDELSBRUNNER, H.; KIRKPATRICK, D. G.; SEIDEL, S. On the shape of set of points in the plane. IEEE Transactions on Information Theory, v. IT-29, n. 4, p. 551-559, 1983.

GALVANIN, E. A. S.; DAL POZ, A. P. Extraction of building roof contours from LiDAR data using a Markov-Random-Field-based approach. IEEE Transactions on Geoscience and Remote Sensing, v. 50, n. 3, 2012. p. 981-987.

GHAMISI, P.; H

GONZÁLEZ-AGUILERA, D.; CRESPO-MATELLÁN, E.; HERNÁNDEZ-L

JOCHEM, A.; HOFLE, B.; RUTZINGER, M.; PFEIFER, N. Automatic roof plane detection and analysis in airborne LiDAR point clouds for solar potential assessment. Sensors, v. 9, p5241-5262, 2009.

JUNG, J.; JWA, Y.; SOHN, G. Implicit regularization for reconstructing 3D building rooftop models using airborne LiDAR data. Sensors, v. 17, p. 27, 2017.

KANG, Z.; YANG, J.; ZHONG, R. A Bayesian-Network-Based classification method integrating airborne LiDAR data with optical images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 10, n. 4, 1651-1661, 2017.

KIM, C.; HABIB, A. Object-based integration of photogrammetric and LiDAR data for automated generation of complex polyhedral building models. Sensors, v. 9, p. 5679-5701, 2009.

KWAK, E.; HABIB, A. Automatic representation and reconstruction of DBM from LiDAR data using Recursive Minimum Bounding Rectangle. ISPRS Journal of Photogrammetry and Remote Sensing, p. 171-191, 2013.

LEE, J.; HAN, S.; BYUN, Y.; KIM, Y. Extraction and regularization of various building boundaries with complex shapes utilizing distribution characteristics of airborne LiDAR points. ETRI Journal, v. 33, n. 4, p. 547-557, 2011.

MACHADO, Á. M. L. Extração automática de contornos de edificações utilizando imagem gerada por câmara digital de pequeno formato e dados LIDAR. Tese de doutorado

MANNO-KOVACS, A.; SZIRANYI, T. Orientation-selective building detection in aerial images. ISPRS Journal of Photogrammetry and Remote Sensing, p. 94-112, 2015.

MENDES, T. S. G.; DAL POZ, A. P. Integração de imagem aérea de alta resolução e dados de varredura a laser na classificação de cenas urbanas para detectar regiões de via. Boletim de Ciências Geodésicas, v. 19, n. 2, p. 287-312, 2013.

OLIVEIRA, G. R. K. Uso integrado de dados lidar e imagens aéreas aplicado na extração de contornos de telhados de edificações. Dissertação de mestrado

RICHARDS, J. A. Remote sensing digital image analysis: an introduction. Londres: Springer, 2013. 494 p.

SANTOS, R. C.; GALO, M.; CARRILHO, A. C. Building boundary extraction from LiDAR data using a local estimated parameter for alpha-shape algorithm. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLII-1, p. 127-132, 2018a.

SANTOS, R. C.; GALO, M.; CARRILHO, A. C.; PESSOA, G. G. Uso do algoritmo alpha shape na extração de contornos de edificações a partir de dados LiDAR. In: Anais do COBRAC 2018b, Florianópolis

SATARI, M. A Multi-resolution hybrid approach for building model reconstruction from LIDAR data. The Photogrammetric Record. v. 27, n. 139, p. 330-359, 2012.

SHEN, W. Building boundary extraction based on LiDAR point clouds data. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Beijing, China, v. 37, p. 157-162, 2008.

SOKOLOVA, M.; JAPKOWICZ, N.; SZPAKOWICZ, S. Beyond accuracy, f-score and roc: a family of discriminant measures for performance evaluation. In: Proceedings of the AAAI

TOMMASELLI, A. M. G.; GALO, M.; DOS REIS, T. T.; RUY, R.; MORAES, M. V. A.; MATRICARDI, W. V. Development and assessment of a data set containing frame images and dense airborne laser scanning point clouds. IEEE Geoscience and Remote Sensing Letters, v. 15, p. 192-196, 2018.

WANG, J.; YANG, X.; QIN, X.; YE, X.; QIN, Q. An efficient approach for automatic rectangular building extraction from very high resolution optical satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, v.12, n. 3, p. 487-491, 2015.

YOUSEFHUSSIEN, M.; KELBE, D. J.; IENTILUCCI, E. J.; SALVAGGIO, C. A multi-scale fully convolutional network for semantic labeling of 3D point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, [S.l.: s.n.], 2018, 14 p.

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