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A Method for the Automated Construction of 3D Models of Cities and Neighborhoods from Official Cadaster Data for Solar Analysis

Author

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  • Carlos Beltran-Velamazan

    (Department of Architecture, University of Zaragoza, 50 108 Zaragoza, Spain)

  • Marta Monzón-Chavarrías

    (Department of Architecture, University of Zaragoza, 50 108 Zaragoza, Spain)

  • Belinda López-Mesa

    (Department of Architecture, University of Zaragoza, 50 108 Zaragoza, Spain)

Abstract

3D city models are a useful tool to analyze the solar potential of neighborhoods and cities. These models are built from buildings footprints and elevation measurements. Footprints are widely available, but elevation datasets remain expensive and time-consuming to acquire. Our hypothesis is that the GIS cadastral data can be used to build a 3D model automatically, so that generating complete cities 3D models can be done in a short time with already available data. We propose a method for the automatic construction of 3D models of cities and neighborhoods from 2D cadastral data and study their usefulness for solar analysis by comparing the results with those from a hand-built model. The results show that the accuracy in evaluating solar access on pedestrian areas and solar potential on rooftops with the automatic method is close to that from the hand-built model with slight differences of 3.4% and 2.2%, respectively. On the other hand, time saving with the automatic models is significant. A neighborhood of 400,000 m 2 can be built up in 30 min, 50 times faster than by hand, and an entire city of 967 km 2 can be built in 8.5 h.

Suggested Citation

  • Carlos Beltran-Velamazan & Marta Monzón-Chavarrías & Belinda López-Mesa, 2021. "A Method for the Automated Construction of 3D Models of Cities and Neighborhoods from Official Cadaster Data for Solar Analysis," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6028-:d:563177
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    References listed on IDEAS

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    1. Zhang, Yuhu & Ren, Jing & Pu, Yanru & Wang, Peng, 2020. "Solar energy potential assessment: A framework to integrate geographic, technological, and economic indices for a potential analysis," Renewable Energy, Elsevier, vol. 149(C), pages 577-586.
    2. Zhu, Rui & Wong, Man Sing & You, Linlin & Santi, Paolo & Nichol, Janet & Ho, Hung Chak & Lu, Lin & Ratti, Carlo, 2020. "The effect of urban morphology on the solar capacity of three-dimensional cities," Renewable Energy, Elsevier, vol. 153(C), pages 1111-1126.
    3. Sarawut Ninsawat & Mohammad Dalower Hossain, 2016. "Identifying Potential Area and Financial Prospects of Rooftop Solar Photovoltaics (PV)," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    4. 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).
    5. Hofierka, Jaroslav & Kaňuk, Ján, 2009. "Assessment of photovoltaic potential in urban areas using open-source solar radiation tools," Renewable Energy, Elsevier, vol. 34(10), pages 2206-2214.
    6. Wróżyński, Rafał & Sojka, Mariusz & Pyszny, Krzysztof, 2016. "The application of GIS and 3D graphic software to visual impact assessment of wind turbines," Renewable Energy, Elsevier, vol. 96(PA), pages 625-635.
    7. Mohajeri, N. & Gudmundsson, A. & Kunckler, T. & Upadhyay, G. & Assouline, D. & Kämpf, J.H & Scartezzini, J.L., 2019. "A solar-based sustainable urban design: The effects of city-scale street-canyon geometry on solar access in Geneva, Switzerland," Applied Energy, Elsevier, vol. 240(C), pages 173-190.
    8. Mohajeri, Nahid & Upadhyay, Govinda & Gudmundsson, Agust & Assouline, Dan & Kämpf, Jérôme & Scartezzini, Jean-Louis, 2016. "Effects of urban compactness on solar energy potential," Renewable Energy, Elsevier, vol. 93(C), pages 469-482.
    9. Cheng, Liang & Zhang, Fangli & Li, Shuyi & Mao, Junya & Xu, Hao & Ju, Weimin & Liu, Xiaoqiang & Wu, Jie & Min, Kaifu & Zhang, Xuedong & Li, Manchun, 2020. "Solar energy potential of urban buildings in 10 cities of China," Energy, Elsevier, vol. 196(C).
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    Cited by:

    1. Agnieszka Bieda & Agnieszka Cienciała, 2021. "Towards a Renewable Energy Source Cadastre—A Review of Examples from around the World," Energies, MDPI, vol. 14(23), pages 1-34, December.
    2. Tian, B. & Loonen, R.C.G.M. & Bognár, Á. & Hensen, J.L.M., 2022. "Impacts of surface model generation approaches on raytracing-based solar potential estimation in urban areas," Renewable Energy, Elsevier, vol. 198(C), pages 804-824.

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