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Abstract
The identification of roof surfaces is characterized by a sequence of several processing steps. The boundary detection of different types of roof is realized from light detection and ranging (LiDAR) cloud points and can confirm the real boundary of the roof. In the process of processing LiDAR data, shortcomings have been found regarding the inappropriate classification of points (class 6 “buildings”) concerning the roofs (the points of the building facade were marked as outliers and reclassified). In cases of insufficient point density, there is a problem with not being able to capture either the roof boundary or small roof objects, along with the possible occurrence of gaps inside the roof areas. This study proposes a processing procedure in a geographic information system (GIS) environment that advocates the identification of roof surfaces based on the LiDAR point cloud. We created the contours of a roof surface boundary with a simplified regular shape. From 824 roofs in the studied area, six different types of roof were selected, which this study presents in detail. The expected result of the study is the generation of segments inside the roof boundary. The study also includes the visualization of the outcomes of the spatial analyses of the identified roof surfaces, which forms the basis for determining the potential of solar systems with respect to green roofs for the development of smart city buildings.
Highlights
Today, more than two thirds of the union’s population live in cities, consuming around 80% of its energy and producing up to 85% of Europe’s gross domestic product [1]
This study focused on the topic of identifying the roof areas of residential buildings to operatively determine the essential characteristics of these buildings in the construction of smart cities
The framework of this study proposed an emphasis on maintaining the regularity of the roof boundary, regarding the study aim
Summary
More than two thirds of the union’s population live in cities, consuming around 80% of its energy and producing up to 85% of Europe’s gross domestic product [1]. The 3D city model is an essential part of several different applications and analyses related to infrastructure [6,7], planning and urbanism [8,9], solar radiation modeling in urban areas [10,11], and the potential for building green roofs in cities [12,13], etc These location models are often not available, are of low image quality, or the data is out of date. Traditional methods of data collection using photogrammetry for the creation of 3D models of objects have been replaced in recent years by LiDAR aerial laser scanning and the use of special processing techniques and procedures [20]. Tcihtye adpisptrliiccatstioorneonfttihree ccihtioesse. nThmeemthaoidnobloegnyefiistspoofssthibislesntuodt yonalrye:ina acrsimticaalll vaireewa boufttahlesoerartotrhseinletvheel oinfpcuittyddaitsat;ritchtes aonr aelnytsiirseocfittihese. rTashteermcaeilnl sbizeeneefinttseroifngthtihs estpurdoyceasrsei:nga; ctrhieticdaelsvigienwofofththeeexetrrraocrtsioinn tphreocinedpuurtedoaftar;otohfeoabnjeacltyssifsorofptrhoecersassitnegr cineltlhseizGeIeSnetnerviinrgonthmeepnrto; caeustsoimnga;titohne dtoescirgenatoefathdeeteaxilteradcrtoioonf pmroodceedl;uthreeovfisruoaolfizoabtjieocntsoffoarnparlyocseesssoinf sgeilnecttheedGgeISomenevtriircoannmdetnotp; oaulotgoimcaaltpioanratmo certeearsteoaf rdoeotfasiluerdfarcoeosf, mwiotdhetlh; ethpeovssisiublaeliazpatpiloincaotifoannianlytsheesfoieflsdeolefcpteodtegnetoiaml detertiecramnidnatotipoonlofogricsaollpararsaymsteetmerssaonfdrogorfeesnurrfoaocefss., with the possible application in the field of potential determination for solar systems and green roofs
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