Articles | Volume 13, issue 11
https://doi.org/10.5194/essd-13-5389-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-5389-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
Hou Jiang
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Southern Marine Science and Engineering Guangdong Laboratory,
Guangzhou 511458, China
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing Normal University,
Nanjing 210023, China
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Southern Marine Science and Engineering Guangdong Laboratory,
Guangzhou 511458, China
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing Normal University,
Nanjing 210023, China
Jun Qin
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Southern Marine Science and Engineering Guangdong Laboratory,
Guangzhou 511458, China
Tang Liu
School of Information Engineering, China University of Geosciences
(Beijing), Beijing 100083, China
Yujun Liu
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Provincial Geomatics Center of Jiangsu, Nanjing 210013, China
Chenghu Zhou
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
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Cited articles
Ball, J. E., Anderson, D. T., and Chan, C. S.: Comprehensive survey of deep
learning in remote sensing: theories, tools, and challenges for the
community, J. Appl. Remote Sens., 11, 042609, https://doi.org/10.1117/1.JRS.11.042609,
2017.
Bódis, K., Kougias, I., Jäger-Waldau, A., Taylor, N., and
Szabó, S.: A high-resolution geospatial assessment of the rooftop solar
photovoltaic potential in the European Union, Renew. Sust. Energ. Rev., 114,
109309, https://doi.org/10.1016/j.rser.2019.109309, 2019.
Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, in: Computer Vision – ECCV 2018, edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., Springer, Cham, Germany, 833–851, https://doi.org/10.1007/978-3-030-01234-2_49, 2018.
Chu, S. and Majumdar, A.: Opportunities and challenges for a sustainable
energy future, Nature, 488, 294–303, https://doi.org/10.1038/nature11475, 2012.
Golovko, V., Bezobrazov, S., Kroshchanka, A., Sachenko, A., Komar, M., and
Karachka, A.: Convolutional neural network based solar photovoltaic panel
detection in satellite photos, 2017 9th IEEE International Conference on
Intelligent Data Acquisition and Advanced Computing Systems: Technology and
Applications (IDAACS), Bucharest, Romania, 21–23 September 2017, 14–19, https://doi.org/10.1109/IDAACS.2017.8094501, 2017.
Hernandez, R. R., Hoffacker, M. K., Murphy-Mariscal, M. L., Wu, G. C., and
Allen, M. F.: Solar energy development impacts on land cover change and
protected areas, P. Natl. Acad. Sci. USA, 112, 13579,
https://doi.org/10.1073/pnas.1517656112, 2015.
House, D., Lech, M., and Stolar, M.: Using deep learning to identify
potential roof spaces for solar panels, 2018 12th International
Conference on Signal Processing and Communication Systems (ICSPCS), Cairns, Australia, 17–19 December 2018, 1–6,
https://doi.org/10.1109/ICSPCS.2018.8631725, 2018.
IRENA: Renewable capacity statistics 2021, International Renewable Energy
Agency (IRENA), Abu Dhabi, 2021.
Ji, S., Wei, S., and Lu, M.: Fully convolutional networks for multisource
building extraction from an open aerial and satellite imagery data set, IEEE
T. Geosci. Remote, 57, 574–586, https://doi.org/10.1109/TGRS.2018.2858817, 2019.
Ji, S., Zhang, Z., Zhang, C., Wei, S., Lu, M., and Duan, Y.: Learning
discriminative spatiotemporal features for precise crop classification from
multi-temporal satellite images, Int. J. Remote Sens., 41, 3162–3174,
https://doi.org/10.1080/01431161.2019.1699973, 2020.
Jiang, H., Yao, L., and Liu, Y.: Multi-resolution dataset for photovoltaic
panel segmentation from satellite and aerial imagery, Zenodo [data set],
https://doi.org/10.5281/zenodo.5171712, 2021.
Kabir, E., Kumar, P., Kumar, S., Adelodun, A. A., and Kim, K.-H.: Solar
energy: Potential and future prospects, Renew. Sust. Energ. Rev., 82,
894–900, https://doi.org/10.1016/j.rser.2017.09.094, 2018.
La Monaca, S. and Ryan, L.: Solar PV where the sun doesn't shine:
Estimating the economic impacts of support schemes for residential PV with
detailed net demand profiling, Energ. Policy, 108, 731–741,
https://doi.org/10.1016/j.enpol.2017.05.052, 2017.
Li, K., Wan, G., Cheng, G., Meng, L., and Han, J.: Object detection in
optical remote sensing images: A survey and a new benchmark, ISPRS J.
Photogramm., 159, 296–307, https://doi.org/10.1016/j.isprsjprs.2019.11.023, 2020.
Liang, S., Qi, F., Ding, Y., Cao, R., Yang, Q., and Yan, W.: Mask R-CNN
based segmentation method for satellite imagery of photovoltaics generation
systems, 2020 39th Chinese Control Conference (CCC), Shenyang, China, 27–29 July 2020, 5343–5348,
https://doi.org/10.23919/CCC50068.2020.9189474, 2020.
Lin, G., Milan, A., Shen, C., and Reid, I.: RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, USA, 21–26 July 2017, 5168–5177, https://doi.org/10.1109/CVPR.2017.549, 2017.
Liu, L., Sun, Q., Li, H., Yin, H., Ren, X., and Wennersten, R.: Evaluating
the benefits of Integrating Floating Photovoltaic and Pumped Storage Power
System, Energ. Convers. Manage., 194, 173–185,
https://doi.org/10.1016/j.enconman.2019.04.071, 2019.
Majumdar, D. and Pasqualetti, M. J.: Analysis of land availability for
utility-scale power plants and assessment of solar photovoltaic development
in the state of Arizona, USA, Renew. Energ., 134, 1213–1231,
https://doi.org/10.1016/j.renene.2018.08.064, 2019.
Malof, J. M., Rui, H., Collins, L. M., Bradbury, K., and Newell, R.: Automatic
solar photovoltaic panel detection in satellite imagery, 2015
International Conference on Renewable Energy Research and Applications
(ICRERA), 1428–1431, Palermo, Italy, 22–25 November 2015, https://doi.org/10.1109/ICRERA.2015.7418643, 2015.
Martins, F. R., Pereira, E. B., and Abreu, S. L.: Satellite-derived solar
resource maps for Brazil under SWERA project, Sol. Energy, 81, 517–528,
https://doi.org/10.1016/j.solener.2006.07.009, 2007.
Moutinho, V. and Robaina, M.: Is the share of renewable energy sources
determining the CO2 kWh and income relation in electricity generation?,
Renew. Sust. Energ. Rev., 65, 902–914, https://doi.org/10.1016/j.rser.2016.07.007,
2016.
Perez, R., Kmiecik, M., Herig, C., and Renné, D.: Remote monitoring of
PV performance using geostationary satellites, Sol. Energy, 71, 255–261,
https://doi.org/10.1016/S0038-092X(01)00050-0, 2001.
Peters, I. M., Liu, H., Reindl, T., and Buonassisi, T.: Global prediction of
photovoltaic field performance differences using open-source satellite data,
Joule, 2, 307–322, https://doi.org/10.1016/j.joule.2017.11.012, 2018.
Rabaia, M. K. H., Abdelkareem, M. A., Sayed, E. T., Elsaid, K., Chae, K.-J.,
Wilberforce, T., and Olabi, A. G.: Environmental impacts of solar energy
systems: A review, Sci. Total Environ., 754, 141989,
https://doi.org/10.1016/j.scitotenv.2020.141989, 2021.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019.
Rico Espinosa, A., Bressan, M., and Giraldo, L. F.: Failure signature
classification in solar photovoltaic plants using RGB images and
convolutional neural networks, Renew. Energ., 162, 249–256,
https://doi.org/10.1016/j.renene.2020.07.154, 2020.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by: Navab N., Hornegger J., Wells W., and Frangi A., Springer, Cham, Germany, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015.
Sacchelli, S., Garegnani, G., Geri, F., Grilli, G., Paletto, A., Zambelli,
P., Ciolli, M., and Vettorato, D.: Trade-off between photovoltaic systems
installation and agricultural practices on arable lands: An environmental
and socio-economic impact analysis for Italy, Land Use Policy, 56, 90–99,
https://doi.org/10.1016/j.landusepol.2016.04.024, 2016.
Shin, H., Hansen, K. U., and Jiao, F.: Techno-economic assessment of
low-temperature carbon dioxide electrolysis, Nat. Sustain., 4, 911–919,
https://doi.org/10.1038/s41893-021-00739-x, 2021.
Song, Y., Wu, W., Liu, Z., Yang, X., Liu, K., and Lu, W.: An Adaptive Pansharpening Method by Using Weighted Least Squares Filter, IEEE Geosci. Remote. Sens. Lett., 13, 18–22, https://doi.org/10.1109/LGRS.2015.2492569, 2016.
Wang, M., Cui, Q., Sun, Y., and Wang, Q.: Photovoltaic panel extraction from
very high-resolution aerial imagery using region–line primitive association
analysis and template matching, ISPRS J. Photogramm., 141, 100–111,
https://doi.org/10.1016/j.isprsjprs.2018.04.010, 2018.
Xia, G., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M.,
Pelillo, M., and Zhang, L.: DOTA: A large-scale dataset for object detection
in aerial images, 2018 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, Salt Lake City, USA, 18–23 June 2018, 3974–3983, https://doi.org/10.1109/CVPR.2018.00418, 2018.
Yan, J. Y., Yang, Y., Campana, P. E., and He, J. J.: City-level analysis of
subsidy-free solar photovoltaic electricity price, profits and grid parity
in China, Nat. Energy, 4, 709–717, https://doi.org/10.1038/s41560-019-0441-z, 2019.
Yao, Y. and Hu, Y.: Recognition and location of solar panels based on
machine vision, 2017 2nd Asia-Pacific Conference on Intelligent Robot
Systems (ACIRS), Wuhan, China, 16–19 June 2017, 7–12, https://doi.org/10.1109/ACIRS.2017.7986055, 2017.
Yu, J., Wang, Z., Majumdar, A., and Rajagopal, R.: DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States, Joule, 2, 2605–2617, https://doi.org/10.1016/j.joule.2018.11.021, 2018.
Zambrano-Asanza, S., Quiros-Tortos, J., and Franco, J. F.: Optimal site
selection for photovoltaic power plants using a GIS-based multi-criteria
decision making and spatial overlay with electric load, Renew. Sust. Energ.
Rev., 143, 110853, https://doi.org/10.1016/j.rser.2021.110853, 2021.
Short summary
A multi-resolution (0.8, 0.3, and 0.1 m) photovoltaic (PV) dataset is established using satellite and aerial images. The dataset contains 3716 samples of PVs installed on various land and rooftop types. The dataset can support multi-scale PV segmentation (e.g., concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs) and cross applications between different resolutions (e.g., from satellite to aerial samples and vice versa), as well as other research related to PVs.
A multi-resolution (0.8, 0.3, and 0.1 m) photovoltaic (PV) dataset is established using...
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