IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v262y2026ics0960148126001679.html

City-scale estimation of actual available rooftop areas for solar photovoltaic using a two-stage SegFormer based model

Author

Listed:
  • Dong, Lingshuang
  • Xie, Jinfeng
  • Zhang, Yi

Abstract

Development of rooftop photovoltaic (RPV) systems in the urban area plays a crucial role in sustainable energy transformation. However, few studies have accurately assessed RPV potential with the consideration of various superstructures that prevent installation. To address this gap, this study presents a Two-Stage Available RPV Area Extraction (TS-ARPVAE) model based on SegFormer to precisely identify areas suitable for RPV installation in complex urban environments. The proposed model comprises two stages: RoofSeg for extracting roof boundaries and AvailSeg for identifying available areas by excluding rooftop superstructures. To support model development, we established the first comprehensive dataset focusing on available RPV areas in urban contexts, featuring diverse rooftop categories and standardized annotation protocols. The model achieves 93.28 % accuracy, outperforming traditional single-stage approaches. Applied to Shenzhen, China, our analysis reveals that 110.5 km2 (64 %) of the total 170.9 km2 rooftop area is suitable for RPV installation, with industrial and warehousing buildings showing the highest installation potential. Buildings with larger roof areas exhibit higher percentages of available RPV area. Across Shenzhen's nine districts, the availability percentages range from 56.24 % to 76.82 %. Shenzhen's estimated rooftop solar capacity could reach 22.1 GW, with potential generation of 19,028.8 GWh in 2024.

Suggested Citation

  • Dong, Lingshuang & Xie, Jinfeng & Zhang, Yi, 2026. "City-scale estimation of actual available rooftop areas for solar photovoltaic using a two-stage SegFormer based model," Renewable Energy, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:renene:v:262:y:2026:i:c:s0960148126001679
    DOI: 10.1016/j.renene.2026.125342
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148126001679
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2026.125342?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Li, Guanglei & Wang, Guohao & Luo, Tengqi & Hu, Yuxiao & Wu, Shouyuan & Gong, Guanghui & Song, Chenchen & Guo, Zhiling & Liu, Zhengguang, 2024. "SolarSAM: Building-scale photovoltaic potential assessment based on Segment Anything Model (SAM) and remote sensing for emerging city," Renewable Energy, Elsevier, vol. 237(PA).
    2. Yang, Jinhao & Wu, Jinghua & Lu, Junjie & Peng, Xiangang & Yuan, Haoliang & Lai, Loi Lei, 2024. "A novel method for assessment rooftop PV potential based on remote sensing images," Renewable Energy, Elsevier, vol. 237(PC).
    3. Mohajeri, Nahid & Assouline, Dan & Guiboud, Berenice & Bill, Andreas & Gudmundsson, Agust & Scartezzini, Jean-Louis, 2018. "A city-scale roof shape classification using machine learning for solar energy applications," Renewable Energy, Elsevier, vol. 121(C), pages 81-93.
    4. Sun, Honghang & Zhi, Qiang & Wang, Yibo & Yao, Qiang & Su, Jun, 2014. "China’s solar photovoltaic industry development: The status quo, problems and approaches," Applied Energy, Elsevier, vol. 118(C), pages 221-230.
    5. Khan, Jibran & Arsalan, Mudassar Hassan, 2016. "Estimation of rooftop solar photovoltaic potential using geo-spatial techniques: A perspective from planned neighborhood of Karachi – Pakistan," Renewable Energy, Elsevier, vol. 90(C), pages 188-203.
    6. Zhong, Teng & Zhang, Zhixin & Chen, Min & Zhang, Kai & Zhou, Zixuan & Zhu, Rui & Wang, Yijie & Lü, Guonian & Yan, Jinyue, 2021. "A city-scale estimation of rooftop solar photovoltaic potential based on deep learning," Applied Energy, Elsevier, vol. 298(C).
    7. Vardimon, Ran, 2011. "Assessment of the potential for distributed photovoltaic electricity production in Israel," Renewable Energy, Elsevier, vol. 36(2), pages 591-594.
    8. Wei, Tianxi & Zhang, Yi & Zhang, Yuhang & Miao, Rui & Kang, Jian & Qi, He, 2024. "City-scale roof-top photovoltaic deployment planning," Applied Energy, Elsevier, vol. 368(C).
    9. Aslani, Mohammad & Seipel, Stefan, 2022. "Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment," Applied Energy, Elsevier, vol. 306(PA).
    10. Ordóñez, J. & Jadraque, E. & Alegre, J. & Martínez, G., 2010. "Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 2122-2130, September.
    11. Berwal, Anil K. & Kumar, Sanjay & Kumari, Nisha & Kumar, Virender & Haleem, Abid, 2017. "Design and analysis of rooftop grid tied 50kW capacity Solar Photovoltaic (SPV) power plant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1288-1299.
    12. Li, Qingyu & Krapf, Sebastian & Mou, Lichao & Shi, Yilei & Zhu, Xiao Xiang, 2024. "Deep learning-based framework for city-scale rooftop solar potential estimation by considering roof superstructures," Applied Energy, Elsevier, vol. 374(C).
    13. Ren, Haoshan & Sun, Yongjun & Norman Tse, Chung Fai & Fan, Cheng, 2023. "Optimal packing and planning for large-scale distributed rooftop photovoltaic systems under complex shading effects and rooftop availabilities," Energy, Elsevier, vol. 274(C).
    14. Primož Mavsar & Klemen Sredenšek & Bojan Štumberger & Miralem Hadžiselimović & Sebastijan Seme, 2019. "Simplified Method for Analyzing the Availability of Rooftop Photovoltaic Potential," Energies, MDPI, vol. 12(22), pages 1-17, November.
    15. Thai, Clinton & Brouwer, Jack, 2021. "Challenges estimating distributed solar potential with utilization factors: California universities case study," Applied Energy, Elsevier, vol. 282(PB).
    16. Boccalatte, Alessia & Chanussot, Jocelyn, 2025. "Quantifying urban solar potential losses from rooftop superstructures via aerial imagery and Convolutional Neural Networks," Renewable Energy, Elsevier, vol. 249(C).
    17. Hafez, A.Z. & Soliman, A. & El-Metwally, K.A. & Ismail, I.M., 2017. "Tilt and azimuth angles in solar energy applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 147-168.
    18. 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).
    Full references (including those not matched with items on IDEAS)

    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. Li, Qingyu & Krapf, Sebastian & Mou, Lichao & Shi, Yilei & Zhu, Xiao Xiang, 2024. "Deep learning-based framework for city-scale rooftop solar potential estimation by considering roof superstructures," Applied Energy, Elsevier, vol. 374(C).
    2. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).
    3. Mao, Hongzhi & Liu, Weili & Li, Chongzheng & Tian, Zhiyong & Zarrella, Angelo & Ma, Ling & Chen, Xinyu & Luo, Yongqiang & Fan, Jianhua, 2025. "An accurate quantification study on the rooftop PV potential based UAV field photography in dense urban environments," Applied Energy, Elsevier, vol. 399(C).
    4. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    5. Xu, Jian & Guo, Zhiling & Yu, Qing & Dong, Kechuan & Tan, Hongjun & Zhang, Haoran & Yan, Jinyue, 2025. "Spatiotemporal feature encoded deep learning method for rooftop PV potential assessment," Applied Energy, Elsevier, vol. 394(C).
    6. 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).
    7. Drozd, Paweł & Kapica, Jacek & Jurasz, Jakub & Dąbek, Paweł, 2025. "Evaluating cities' solar potential using geographic information systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 209(C).
    8. Ren, Haoshan & Xu, Chengliang & Ma, Zhenjun & Sun, Yongjun, 2022. "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities," Applied Energy, Elsevier, vol. 306(PA).
    9. Lodhi, Muhammad Kamran & Tan, Yumin & Wang, Xiaolu & Masum, Syed Muhammad & Nouman, Khan Muhammad & Ullah, Nasim, 2024. "Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach," Energy, Elsevier, vol. 304(C).
    10. Zhong, Teng & Zhang, Zhixin & Chen, Min & Zhang, Kai & Zhou, Zixuan & Zhu, Rui & Wang, Yijie & Lü, Guonian & Yan, Jinyue, 2021. "A city-scale estimation of rooftop solar photovoltaic potential based on deep learning," Applied Energy, Elsevier, vol. 298(C).
    11. Hou Jiang & Ning Lu & Xuecheng Wang, 2023. "Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    12. Zaragozí, Benito & Salvat-Garcia, Lluís & Saladié-Gil, Sergi & Saladié, Òscar, 2025. "Harnessing rooftop photovoltaic potential: Adoption disparities across building types in Tarragona Province, Spain," Utilities Policy, Elsevier, vol. 96(C).
    13. Lukač, Niko & Mongus, Domen & Žalik, Borut & Štumberger, Gorazd & Bizjak, Marko, 2024. "Novel GPU-accelerated high-resolution solar potential estimation in urban areas by using a modified diffuse irradiance model," Applied Energy, Elsevier, vol. 353(PA).
    14. Wei, Tianxi & Zhang, Yi & Zhang, Yuhang & Miao, Rui & Kang, Jian & Qi, He, 2024. "City-scale roof-top photovoltaic deployment planning," Applied Energy, Elsevier, vol. 368(C).
    15. Aslani, Mohammad & Seipel, Stefan, 2022. "Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment," Applied Energy, Elsevier, vol. 306(PA).
    16. Boccalatte, Alessia & Chanussot, Jocelyn, 2025. "Quantifying urban solar potential losses from rooftop superstructures via aerial imagery and Convolutional Neural Networks," Renewable Energy, Elsevier, vol. 249(C).
    17. Dong, Kechuan & Guo, Zhiling & Yu, Qing & Xu, Jian & Yan, Jinyue, 2026. "Data-driven prediction of fine-grained facade solar irradiance for urban PV potential assessment," Applied Energy, Elsevier, vol. 403(PB).
    18. Molnár, Gergely & Cabeza, Luisa F. & Chatterjee, Souran & Ürge-Vorsatz, Diana, 2024. "Modelling the building-related photovoltaic power production potential in the light of the EU's Solar Rooftop Initiative," Applied Energy, Elsevier, vol. 360(C).
    19. Jiang, Hou & Yao, Ling & Lu, Ning & Qin, Jun & Zhang, Xiaotong & Liu, Tang & Zhang, Xingxing & Zhou, Chenghu, 2024. "Exploring the optimization of rooftop photovoltaic scale and spatial layout under curtailment constraints," Energy, Elsevier, vol. 293(C).
    20. Jiang, Hou & Zhang, Xiaotong & Yao, Ling & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2023. "High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles," Applied Energy, Elsevier, vol. 348(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:eee:renene:v:262:y:2026:i:c:s0960148126001679. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.