Convolutional neural network based solar photovoltaic panel detection in satellite photos

The aim of this work is the detection of solar photovoltaic panels in low-quality satellite photos. It is important to receive the geospatial data (such as country, zip code, street and home number) of installed solar panels, because they are connected directly to the local power. It will be helpful...

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Bibliographic Details
Published in2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Vol. 1; pp. 14 - 19
Main Authors Golovko, Vladimir, Bezobrazov, Sergei, Kroshchanka, Alexander, Sachenko, Anatoliy, Komar, Myroslav, Karachka, Andriy
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
Biological neural networks
convolutional neural network
energy production
Feature extraction
geospatial data
Neurons
power capacity
satellite photos
Satellites
Solar panels
solar panels detection
Training
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Summary:The aim of this work is the detection of solar photovoltaic panels in low-quality satellite photos. It is important to receive the geospatial data (such as country, zip code, street and home number) of installed solar panels, because they are connected directly to the local power. It will be helpful to estimate a power capacity and an energy production using the satellite photos. For this purpose, a Convolutional Neural Network was used. For training and testing dataset consists of 3347 low-quality Google satellite images was used. The experimental results show a high rate accuracy of detection with low rate incorrect classifications of the proposed approach. The proposed approach has enormous implementation and can be improved in future.
DOI:10.1109/IDAACS.2017.8094501