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Deep Convolutional Neural Network for Detection of Solar Panels

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Data-Centric Business and Applications

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 48))

Abstract

The article describes a method for detecting solar panels in satellite imagery. Due to the growing popularity of this technology, problems associated with the maintenance of solar panels are also becoming relevant. Many service companies are interested in obtaining information about potential customers. Thus, the analysis of photographs in order to identify solar panels and accumulate statistical information on energy production capacities, their territorial distribution is an urgent task. The development of the theory for learning the deep neural networks gave a powerful impact on the development of various versions of convolutional neural networks used to solve recognition and classification issues. The advent of GPU parallelization technology has made such training feasible in a reasonable amount of time. In this article authors solved the two independent problems. A first, the deep convolutional neural network recognizes the presence of solar panels in a photograph. This model was trained using a set of low-resolution photos from low-quality satellite images from Google and showed a high result of detecting the presence of an object in the image. A second, solar panels with obtaining the exact coordinates of their location were detected.

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Acknowledgements

This work is performed under a grant by the Ministry of Education and Sciences, Ukraine, 2018–2019 as well as it’s supported by the Belarusian State Research Program “Informatics and Space”.

Author information

Authors and Affiliations

  1. Brest State Technical University, Brest, Belarus

    Vladimir Golovko, Alexander Kroshchanka & Egor Mikhno

  2. Państwowa Szkoła Wyższa im. Papieża Jana Pawła II, Biala Podlaska, Poland

    Vladimir Golovko

  3. Ternopil National Economic University, Ternopil, Ukraine

    Myroslav Komar & Anatoliy Sachenko

  4. University of Information Technology and Management, Rzeszow, Poland

    Anatoliy Sachenko

Authors
  1. Vladimir Golovko
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  2. Alexander Kroshchanka
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  3. Egor Mikhno
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  4. Myroslav Komar
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  5. Anatoliy Sachenko
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Corresponding author

Correspondence to Myroslav Komar .

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Editors and Affiliations

  1. V.V. Popovskyy department of Infocommunication Engineering, Faculty of Infocommunication, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Tamara Radivilova

  2. V.V. Popovskyy department of Infocommunication Engineering, Faculty of Infocommunication, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Dmytro Ageyev

  3. Department of e-Business, Faculty of Business, Economics and Statistics, University of Vienna, Vienna, Austria

    Natalia Kryvinska

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Golovko, V., Kroshchanka, A., Mikhno, E., Komar, M., Sachenko, A. (2021). Deep Convolutional Neural Network for Detection of Solar Panels. In: Radivilova, T., Ageyev, D., Kryvinska, N. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-030-43070-2_17

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