Articles | Volume 13, issue 11
https://doi.org/10.5194/essd-13-5389-2021
https://doi.org/10.5194/essd-13-5389-2021
Data description paper
 | 
19 Nov 2021
Data description paper |  | 19 Nov 2021

Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery

Hou Jiang, Ling Yao, Ning Lu, Jun Qin, Tang Liu, Yujun Liu, and Chenghu Zhou

Related authors

A long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations
Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou
Earth Syst. Sci. Data, 15, 331–344, https://doi.org/10.5194/essd-15-331-2023,https://doi.org/10.5194/essd-15-331-2023, 2023
Short summary
Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data
Hou Jiang, Ning Lu, Jun Qin, and Ling Yao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-209,https://doi.org/10.5194/essd-2019-209, 2019
Revised manuscript not accepted
Short summary

Related subject area

Energy and Emissions
Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021)
Mark Omara, Anthony Himmelberger, Katlyn MacKay, James P. Williams, Joshua Benmergui, Maryann Sargent, Steven C. Wofsy, and Ritesh Gautam
Earth Syst. Sci. Data, 16, 3973–3991, https://doi.org/10.5194/essd-16-3973-2024,https://doi.org/10.5194/essd-16-3973-2024, 2024
Short summary
State of Wildfires 2023–2024
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024,https://doi.org/10.5194/essd-16-3601-2024, 2024
Short summary
Global Emissions Inventory from Open Biomass Burning (GEIOBB): utilizing Fengyun-3D global fire spot monitoring data
Yang Liu, Jie Chen, Yusheng Shi, Wei Zheng, Tianchan Shan, and Gang Wang
Earth Syst. Sci. Data, 16, 3495–3515, https://doi.org/10.5194/essd-16-3495-2024,https://doi.org/10.5194/essd-16-3495-2024, 2024
Short summary
A global forest burn severity dataset from Landsat imagery (2003–2016)
Kang He, Xinyi Shen, and Emmanouil N. Anagnostou
Earth Syst. Sci. Data, 16, 3061–3081, https://doi.org/10.5194/essd-16-3061-2024,https://doi.org/10.5194/essd-16-3061-2024, 2024
Short summary
Development of a high-resolution integrated emission inventory of air pollutants for China
Nana Wu, Guannan Geng, Ruochong Xu, Shigan Liu, Xiaodong Liu, Qinren Shi, Ying Zhou, Yu Zhao, Huan Liu, Yu Song, Junyu Zheng, Qiang Zhang, and Kebin He
Earth Syst. Sci. Data, 16, 2893–2915, https://doi.org/10.5194/essd-16-2893-2024,https://doi.org/10.5194/essd-16-2893-2024, 2024
Short summary

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. 
Download
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.
Altmetrics
Final-revised paper
Preprint