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Extraction and regularization of building contours from LIDAR data using ALPHA-SHAPE algorithm and principal component analysis

Grant number: 16/20814-0
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): April 01, 2017
Effective date (End): November 30, 2017
Field of knowledge:Physical Sciences and Mathematics - Geosciences
Principal Investigator:Mauricio Galo
Grantee:Renato César dos Santos
Supervisor abroad: Ayman Habib
Home Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
Local de pesquisa : Purdue University, United States  
Associated to the scholarship:16/12167-5 - Extraction and regularization of building contours from LIDAR data using ALPHA-SHAPE algorithm and principal component analysis, BP.DR

Abstract

In the last decades, the processes involved in building extraction from LiDAR data have been the subject of several studies. One of the main interests of the scientific community is related to the generated data set, which can be used to generate or update geographic information systems. 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 3D 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 (PCA) and alpha-shape algorithm. The main goals of this internship in Purdue University are: to improve the proposed novel method for extraction and regularization of building contours; explore the use of eigenvectors as a metric to identify the type of segment (straight-line or curved) and select the points that compose the same segment; and to gain international experience. Beside, the results obtained during the internship will be analyzed through qualitative and quantitative analysis using real data.