Building footprint extraction based on insolation with topographic surface derived from LiDAR dataset. Degree thesis, Universiti Teknologi MARA Cawangan Perlis. (2020)
Abstract
Automatic building extraction has widely been used by many sectors in making their job easier. Example of this sector is land cover mapping, change detection, urban planning, disaster management and many other socioeconomic operations with the combination from remote sensing and GIS methods play an important role in increasing the efficiency of their job. The manual digitization of building outlines is expensive and time-consuming and building footprint extraction is not complete due to covered with trees and cloud that may include the building’s rooftop. The main purpose of this research is identifying the building footprint from all the above ground information captured by LiDAR based on insolation and the objectives is to examine the slope and contour of surface and building roof derived from LiDAR point cloud data, to generate the insolation pattern based on derived topographic surface model, and to identify building footprint based on insolation. The ArcGIS software was used for process the building extraction and identifying characteristic of surface and terrain. The study area was in Taman Melati, Kuala Lumpur. The LiDAR points were used for this research by using solar radiation tools to establish building footprint. The result of this research is the slope and contour of surface and building roof and the building footprint extraction based on the Digital Surface Modelling (DSM). By using building footprint, it is providing the researcher to recognize the potential area for land use planning and urban development and investment in study area.
Item Type: | Thesis (Degree) |
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Keywords: | GIS, Remote sensing, Topography |
Taxonomy: | By Subject > Architecture, Planning & Surveying > Surveying Sciences And Geomatics |
Local Content Hub: | Subjects > Architecture, Planning & Surveying |
Depositing User: | Nur Hayati Abdul Satar |
Date Deposited: | 18 Nov 2020 09:14 |
Last Modified: | 18 Nov 2020 09:14 |
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