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Dealing with Heterogeneous Google Earth Images on Building Area Detection Task

  • Cassio AlmeidaEmail author
  • William Fernandes
  • Simone Barbosa
  • Hélio Lopes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

This paper proposes the use of a convolutional neural network in the building detection task, using as data source the high resolution heterogeneous Google Earth RGB images. This is a challenging task not only because it is necessary to build a training dataset, but also because the learning model has to respond well to the diversity of images taking into account a relatively small training dataset. We use images from Brazil that have a very diverse geography due to their continental dimension. In this case, Google Earth images are very heterogeneous and represent a great challenge. We use an adapted U-net and another convolutional neural network classification method recently proposed in the literature for solving this particular task. The use of U-net shows promising results.

Keywords

Convolution neural network Image segmentation Build detection 

Notes

Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research. We would also like to thank Google Earth and their partners for the images utilized in this study. We also thanks CAPES and CNPq for their partial support.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Cassio Almeida
    • 1
    • 2
    Email author
  • William Fernandes
    • 1
  • Simone Barbosa
    • 1
  • Hélio Lopes
    • 1
  1. 1.Departamento de InformáticaPUC-RioRio de JaneiroBrazil
  2. 2.ENCE-IBGERio de JaneiroBrazil

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