A global inventory of solar photovoltaic energy production units

0
  • 1.

    World Energy Statistical Review 2018 (BP plc., 2018).

  • 2.

    Global Energy Outlook 2018 (International Energy Agency, 2018).

  • 3.

    Renewable capacity statistics 2019 (International Renewable Energy Agency, 2019).

  • 4.

    Byers, L. et al. A global database of power plants (World Resources Institute, 2018).

  • 5.

    Barbose, G. & Darghout, N. Follow the sun (Berkeley Laboratory, 2019); https://openpv.nrel.gov/tracking-the-sun

  • 6.

    Yu, J., Wang, Z., Majumdar, A. & Rajagopal, R. DeepSolar: A Machine Learning Framework for Efficiently Building a Solar Deployment Database in the United States. Joule 2, 2605–2617 (2018).

    Item

    Google Scholar

  • 7.

    Hou, X. et al. Solarnet: an in-depth learning framework for mapping solar power plants in China from satellite images. In ICLR 2020 Wrestling workshop Climate change with machine learning (ICLR, 2020); https://www.climatechange.ai/papers/iclr2020/6.html

  • 8.

    Platts, SG Global Power Plant Database (2018); https://www.spglobal.com/platts/en/products-services/electric-power/world-electric-power-plants-database

  • 9.

    Power stations (IHSMarkit, 2020); https://catalogue.datalake.ihsmarkit.com/

  • ten.

    Renewable energies 2019 (International Energy Agency, 2019); https://www.iea.org/reports/renewables-2019

  • 11.

    Bolinger, M., Seel, J. & Robson, D. Utility Scale Solar: Empirical Trends in US PPA Project Technology, Costs, Performance, and Pricing (Berkeley Laboratory, 2019).

  • 12.

    Fukushima, K. Neocognitron: a self-organized neural network model for a pattern recognition mechanism unaffected by positional change. Biol. Cybern. 36, 193-202 (1980).

    CASE
    Item

    Google Scholar

  • 13.

    LeCun, Y. et al. Backpropagation applied to handwritten postal code recognition. Neural computation. 1, 541-551 (1989).

    Item

    Google Scholar

  • 14.

    Krizhevsky, A., Sutskever, I. & Hinton, GE Imagenet classification with deep convolutional neural networks. In Proc. 25th International Conference on Neural Information Processing Systems (NIPS’12) Flight. 1, 1097-1105 (Curran Associates Inc., 2012).

  • 15.

    Zhang, Z., Liu, Q. & Wang, Y. Deep residual U-Net route extraction. IEEE Geosci. Remote sensing Lett. 15, 749-753 (2018).

    ADS
    CASE
    Item

    Google Scholar

  • 16.

    Ishii, T. et al. Detection by classification of buildings in multispectral satellite imagery. In 2016 23rd International Conference on Pattern Recognition (ICPR) 3344-3349 (IEEE, 2016).

  • 17.

    Audebert, N., Le Saux, B. & Lefévre, S. Beyond RGB: urban remote sensing at very high resolution with deep multimodal networks. ISPRS J. Photogramm. Remote sensing. 140, 20-32 (2018).

    ADS
    Item

    Google Scholar

  • 18.

    Zuo, J., Xu, G., Fu, K., Sun, X. & Sun, H. Segmentation-based aircraft type recognition with deep convolutional neural networks. IEEE Geosci. Remote sensing Lett. 15, 282-286 (2018).

    ADS
    Item

    Google Scholar

  • 19.

    Bentes, C., Velotto, D. & Tings, B. Classification of vessels in TerraSAR-X images with convolutional neural networks. IEEE J. Ocean. Ing. 43, 258-266 (2018).

    ADS
    Item

    Google Scholar

  • 20.

    Gray, M., Watson, L., Ljungwaldh, S. & Morris, E. Nowhere to Hide: Using Satellite Imagery to Estimate the Use of Fossil Fuel Power Plants (Carbon tracker, 2018); https://www.carbontracker.org/reports/nowhere-to-hide/

  • 21.

    Wang, AX, Tran, C., Desai, N., Lobell, D. & Ermon, S. Deep transfer learning for crop yield prediction with remote sensing data. In Proc. 1st ACM SIGCAS conference on IT and sustainable societies 50 (ACM, 2018).

  • 22.

    Wang, H. et al. Comprehensive approach based on deep learning for probabilistic forecasting of wind energy. Appl. Energy 188, 56-70 (2017).

    Item

    Google Scholar

  • 23.

    Imamoglu, N., Kimura, M., Miyamoto, H., Fujita, A. & Nakamura, R. Detection of solar power plants on multispectral satellite imagery using weakly supervised CNN with feedback and m-PCNN fusion functions. Preprint at https://arXiv.org/abs/1704.06410 (2017).

  • 24.

    Malof, JM, Bradbury, K., Collins, LM & Newell, RG Automatic detection of photovoltaic solar panels in high resolution aerial imagery. Appl. Energy 183, 229-240 (2016).

    Item

    Google Scholar

  • 25.

    Camilo, JA, Wang, R., Collins, LM, Bradbury, K. & Malof, JM Application of a semantic segmentation convolutional neural network for precise automatic detection and mapping of photovoltaic solar panels in aerial imagery. Preprint at https://arxiv.org/abs/1801.04018 (2018).

  • 26.

    Badrinarayanan, V., Kendall, A. & Cipolla, R. Segnet: A deep convolution encoder-decoder architecture for image segmentation. Preprint at https://arxiv.org/abs/1511.00561 (2015).

  • 27.

    Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. In International conference on representations of learning (2015); preprint at https://arxiv.org/pdf/1409.1556.pdf

  • 28.

    Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Preprint at https://arxiv.org/abs/1505.04597 (2015).

  • 29.

    He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Preprint at https://arxiv.org/abs/1512.03385 (2015).

  • 30.

    Wu, GC et al. The Power of Place: Land Conservation and Clean Energy Pathways for California (The Nature Conservancy, 2019).

  • 31.

    Konadu, D. et al. Land use implications of future trajectories of energy systems: the case of the UK 2050 carbon plan. Energy policy 86, 328-337 (2015).

    CASE
    Item

    Google Scholar

  • 32.

    Turconi, R., Boldrin, A. & Astrup, T. Life Cycle Assessment (LCA) of Power Generation Technologies: Overview, Comparability and Limitations. Renew. To support. Rev. 28, 555-565 (2013).

    CASE
    Item

    Google Scholar

  • 33.

    Hernandez, R. et al. Large-scale solar energy impacts on the environment. Renew. To support. Rev. 29, 766-779 (2014).

    Item

    Google Scholar

  • 34.

    Bukhary, S., Ahmad, S. & Batista, J. Analysis of Land and Water Requirements for Solar Deployment in the Southwestern United States. Renew. To support. Rev. 82, 3288-3305 (2018).

    Item

    Google Scholar

  • 35.

    Dias, L., Gouveia, JP, Loureno, P. & Seixas, J. Interaction between the potential of photovoltaic systems and the use of agricultural land. Land use policy 81, 725-735 (2019).

    Item

    Google Scholar

  • 36.

    Grodsky, SM & Hernandez, RR Reduction in ecosystem services of desert plants through the development of ground-based solar energy. Nat. To support. 3, 1036-1043 (2020).

    Item

    Google Scholar

  • 37.

    Carlisle, JE, Kane, SL, Solan, D., Bowman, M. & Joe, JC Public attitudes regarding large-scale solar energy development in the United States Renew. To support. Rev. 48, 835-847 (2015).

    Item

    Google Scholar

  • 38.

    Mulvaney, D. Identifying the Roots of the Green Civil War on Large-Scale Solar Power Projects on Public Lands in the American Southwest. J. Land use Sci. 12, 493-515 (2017).

    Item

    Google Scholar

  • 39.

    Lamarche, C. et al. Compilation and validation of SAR and optical data products for a comprehensive and comprehensive inland / ocean water map suitable for the climate modeling community. Remote sensing. 9, 36 (2017).

    ADS
    Item

    Google Scholar

  • 40.

    Cherlet, M. et al. World Atlas of Desertification: Rethinking Land Degradation and Sustainable Land Management (Publications Office of the European Union, 2018).

  • 41.

    Hersbach, H. et al. The global reanalysis of ERA5. QJR Meteorol. Soc. 146, 1999-2049 (2020).

    ADS
    Item

    Google Scholar


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