IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i17p6847-d402936.html
   My bibliography  Save this article

Identification of Roof Surfaces from LiDAR Cloud Points by GIS Tools: A Case Study of Lučenec, Slovakia

Author

Listed:
  • Marcela Bindzarova Gergelova

    (Institute of Geodesy, Cartography and Geographical Information Systems, Faculty of Mining, Ecology, Process Control and Geotechnology, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Slavomir Labant

    (Institute of Geodesy, Cartography and Geographical Information Systems, Faculty of Mining, Ecology, Process Control and Geotechnology, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Stefan Kuzevic

    (Institute of Earth Resources Ecology, Faculty of Mining, Ecology, Process Control and Geotechnology, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Zofia Kuzevicova

    (Institute of Geodesy, Cartography and Geographical Information Systems, Faculty of Mining, Ecology, Process Control and Geotechnology, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Henrieta Pavolova

    (Institute of Earth Resources Ecology, Faculty of Mining, Ecology, Process Control and Geotechnology, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

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.

Suggested Citation

  • Marcela Bindzarova Gergelova & Slavomir Labant & Stefan Kuzevic & Zofia Kuzevicova & Henrieta Pavolova, 2020. "Identification of Roof Surfaces from LiDAR Cloud Points by GIS Tools: A Case Study of Lučenec, Slovakia," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6847-:d:402936
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/17/6847/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/17/6847/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diane Palmer & Elena Koumpli & Ian Cole & Ralph Gottschalg & Thomas Betts, 2018. "A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry," Energies, MDPI, vol. 11(12), pages 1-22, December.
    2. Lingfors, D. & Bright, J.M. & Engerer, N.A. & Ahlberg, J. & Killinger, S. & Widén, J., 2017. "Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis," Applied Energy, Elsevier, vol. 205(C), pages 1216-1230.
    3. Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
    4. Lukač, Niko & Seme, Sebastijan & Žlaus, Danijel & Štumberger, Gorazd & Žalik, Borut, 2014. "Buildings roofs photovoltaic potential assessment based on LiDAR (Light Detection And Ranging) data," Energy, Elsevier, vol. 66(C), pages 598-609.
    5. Haarstad, Håvard & Wathne, Marikken W., 2019. "Are smart city projects catalyzing urban energy sustainability?," Energy Policy, Elsevier, vol. 129(C), pages 918-925.
    6. Miguel Centeno Brito & Paula Redweik & Cristina Catita & Sara Freitas & Miguel Santos, 2019. "3D Solar Potential in the Urban Environment: A Case Study in Lisbon," Energies, MDPI, vol. 12(18), pages 1-13, September.
    7. Hussam Al-Bilbisi, 2019. "Spatial Monitoring of Urban Expansion Using Satellite Remote Sensing Images: A Case Study of Amman City, Jordan," Sustainability, MDPI, vol. 11(8), pages 1-14, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Israel Griol-Barres & Sergio Milla & Antonio Cebrián & Huaan Fan & Jose Millet, 2020. "Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing," Sustainability, MDPI, vol. 12(19), pages 1-22, September.
    2. Marcela Bindzarova Gergelova & Slavomir Labant & Jozef Mizak & Pavel Sustek & Lubomir Leicher, 2021. "Inventory of Locations of Old Mining Works Using LiDAR Data: A Case Study in Slovakia," Sustainability, MDPI, vol. 13(12), pages 1-21, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kristian Skeie & Arild Gustavsen, 2021. "Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling," Energies, MDPI, vol. 14(4), pages 1-32, February.
    2. Sebastian Krapf & Nils Kemmerzell & Syed Khawaja Haseeb Uddin & Manuel Hack Vázquez & Fabian Netzler & Markus Lienkamp, 2021. "Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning," Energies, MDPI, vol. 14(13), pages 1-22, June.
    3. Sredenšek, Klemen & Štumberger, Bojan & Hadžiselimović, Miralem & Mavsar, Primož & Seme, Sebastijan, 2022. "Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method," Energy, Elsevier, vol. 242(C).
    4. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
    5. Lukač, Niko & Špelič, Denis & Štumberger, Gorazd & Žalik, Borut, 2020. "Optimisation for large-scale photovoltaic arrays’ placement based on Light Detection And Ranging data," Applied Energy, Elsevier, vol. 263(C).
    6. Zhong, Qing & Nelson, Jake R. & Tong, Daoqin & Grubesic, Tony H., 2022. "A spatial optimization approach to increase the accuracy of rooftop solar energy assessments," Applied Energy, Elsevier, vol. 316(C).
    7. Walch, Alina & Castello, Roberto & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2020. "Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty," Applied Energy, Elsevier, vol. 262(C).
    8. Rui Zhang & Ruikai Hong & Qiannan Li & Xu He & Age Shama & Jichao Lv & Renzhe Wu, 2025. "Optimizing PV Panel Segmentation in Complex Environments Using Pre-Training and Simulated Annealing Algorithm: The JSWPVI," Land, MDPI, vol. 14(6), pages 1-20, June.
    9. Johari, F. & Lindberg, O. & Ramadhani, U.H. & Shadram, F. & Munkhammar, J. & Widén, J., 2024. "Analysis of large-scale energy retrofit of residential buildings and their impact on the electricity grid using a validated UBEM," Applied Energy, Elsevier, vol. 361(C).
    10. Quitzow, Leslie & Rohde, Friederike, 2022. "Imagining the smart city through smart grids? Urban energy futures between technological experimentation and the imagined low-carbon city," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 59(2), pages 341-359.
    11. Attour, Amel & Baudino, Marco & Krafft, Jackie & Lazaric, Nathalie, 2020. "Determinants of energy tracking application use at the city level: Evidence from France," Energy Policy, Elsevier, vol. 147(C).
    12. Balta, Münevver Özge & Balta, Mustafa Tolga, 2022. "Development of a sustainable hydrogen city concept and initial hydrogen city projects," Energy Policy, Elsevier, vol. 166(C).
    13. Konstantinos Ioannou & Dimitrios Myronidis, 2021. "Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks," Sustainability, MDPI, vol. 13(9), pages 1-15, May.
    14. Ural Kafle & Timothy Anderson & Sunil Prasad Lohani, 2023. "The Potential for Rooftop Photovoltaic Systems in Nepal," Energies, MDPI, vol. 16(2), pages 1-13, January.
    15. Good, Clara & Shepero, Mahmoud & Munkhammar, Joakim & Boström, Tobias, 2019. "Scenario-based modelling of the potential for solar energy charging of electric vehicles in two Scandinavian cities," Energy, Elsevier, vol. 168(C), pages 111-125.
    16. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
    17. Mrówczyńska, Maria & Skiba, Marta & Bazan-Krzywoszańska, Anna & Sztubecka, Małgorzata, 2020. "Household standards and socio-economic aspects as a factor determining energy consumption in the city," Applied Energy, Elsevier, vol. 264(C).
    18. Tatiana Tucunduva Philippi Cortese & Jairo Filho Sousa de Almeida & Giseli Quirino Batista & José Eduardo Storopoli & Aaron Liu & Tan Yigitcanlar, 2022. "Understanding Sustainable Energy in the Context of Smart Cities: A PRISMA Review," Energies, MDPI, vol. 15(7), pages 1-38, March.
    19. Hannes Koch & Stefan Lechner & Sebastian Erdmann & Martin Hofmann, 2022. "Assessing the Potential of Rooftop Photovoltaics by Processing High-Resolution Irradiation Data, as Applied to Giessen, Germany," Energies, MDPI, vol. 15(19), pages 1-17, September.
    20. Yilmaz, Osman Salih & Ateş, Ali Murat & Gülgen, Fatih, 2023. "A novel approach suggestion for assessing the impact of topographic shading on the estimation of the floating photovoltaic technical potential," Energy, Elsevier, vol. 283(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6847-:d:402936. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.