Classifying Houses suitable for Electric Vehicle Charging Point using Neural Network


  • Akash Maurya
  • Prof. Nandini Babbar


remote sensing, electric vehicles, Google Street View, learning


Electric Vehicles (EVS), which appears to be the leading green transport in various countries such as the United Kingdom, it is very important for us to understand that the availability of infrastructure for electric vehicles. In such a multi-disciplinary paper, we demonstrate the workflow with the help of deep learning is to make an automated " urban research, research, research, research to identify the property that is suitable for the charging of electric vehicles. comfortable and easy. A unique open-source database of images for Google Street View (GSV) images were used for training, and to compare them with those of the three-deep neural network, and represents an attempt to distinguish between natural habitats, the availability of street-level imagery. We are going to show you with full-service delivery in the two cities, and the accuracy of 87.2% of the data, and 89.3%, respectively. In this proof-of-concept, showing that it is an exciting new way to use Deep learning in the field of remote sensing, remote sensing, urban planning, and was a significant step in this direction by exploring the use of artificial intelligence, self-confidence, and a built-in strategy guide.




How to Cite

Maurya, A., & Babbar, P. N. (2021). Classifying Houses suitable for Electric Vehicle Charging Point using Neural Network. Journal of Science & Technology (JST), 6(Special Issue 1), 6–10. Retrieved from

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