EXTRACTION AND REGULARIZATION OF BUILDING CONTOURS FROM LiDAR DATA USING ALPHA-SHAPE ALGORITHM AND PRINCIPAL COMPONENT ANALYSIS

• Grant number: 16/12167-5
• Support Opportunities: Scholarships in Brazil - Doctorate
• Effective date (Start): October 01, 2016
• Effective date (End): April 30, 2019
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geodesy
• Principal Investigator: Mauricio Galo
• Grantee: Renato César dos Santos
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
• Associated scholarship(s): 16/20814-0 - EXTRACTION AND REGULARIZATION OF BUILDING CONTOURS FROM LiDAR DATA USING ALPHA-SHAPE ALGORITHM AND PRINCIPAL COMPONENT ANALYSIS, BE.EP.DR

Abstract

The aim of this work is to propose a procedure for extraction and regularization of building roof contours from LiDAR data. The LiDAR data correspond to points sampled by airborne LASER scanning system. The contribution of this project is the development of a methodology that allows the automatic extraction and regularization of straight-line and curved contours in three-dimensional space, since the available methods in the literature have been performed the regularization in 2D space and have not been considered curved segment. To perform the extraction and regularization the idea is to combine principal component analysis and alpha-shape algorithm. Principal component analysis will be applied to calculate the eigenvalues and eigenvectors associated with each LiDAR point and its neighborhood. The eigenvalues will be used to identify the probable edge points by means of a clustering approach, whereas the eigenvectors will be used to identify the type of segment (straight-line or curve) and select the set of points that compose each contour segment. The alpha-shape algorithm will be executed to obtain approximate contour of each building. Regularization will be held by fitting a straight line or a conic curve (parabola, circle or ellipse) on the set of points associated with each segment by Least Square Method. The results generated will be analyzed through qualitative and quantitative analysis. Qualitative analysis will be performed from a visual analysis, while quantitative analysis will be performed using some quality measures, which allow comparing the results with the reference data, such as: root mean square error, completeness and correctness.

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DOS SANTOS, RENATO CESAR; GALO, MAURICIO; CARRILHO, ANDRE CACERES. Extraction of Building Roof Boundaries From LiDAR Data Using an Adaptive Alpha-Shape Algorithm. IEEE Geoscience and Remote Sensing Letters, v. 16, n. 8, p. 1289-1293, AUG 2019. (16/12167-5)

TACHIBANA, VILMA MAYUMI. CLASSIFICATION OF LIDAR DATA OVER BUILDING ROOFS USING K-MEANS AND PRINCIPAL COMPONENT ANALYSIS. Bol. Ciênc. Geod., v. 24, n. 1, p. 69-84, JAN-MAR 2018. (16/12167-5)

DOS SANTOS, RENATO CESAR; GALO, MAURICIO; HABIB, AYMAN F.. Regularization of Building Roof Boundaries from Airborne LiDAR Data Using an Iterative CD-Spline. REMOTE SENSING, v. 12, n. 12, JUN 2020. (16/20814-0, 19/05268-8, 16/12167-5)

DOS SANTOS, RENATO CESAR; PESSOA, GUILHERME GOMES; CARRILHO, ANDRE CACERES; GALO, MAURICIO. Automatic Building Boundary Extraction From Airborne LiDAR Data Robust to Density Variation. IEEE Geoscience and Remote Sensing Letters, v. 19, 2022. (16/12167-5)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

SANTOS, Renato César dos. Extraction and regularization of building roof boundaries from LiDAR data using the alpha-shape algorithm and CD-Spline. 2019. Doctoral Thesis - Universidade Estadual Paulista (Unesp). Faculdade de Ciências e Tecnologia. Presidente Prudente Presidente Prudente.