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Semantic Segmentation for Aerial Mapping

Author

Listed:
  • Gabriel Martinez-Soltero

    (Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, Mexico)

  • Alma Y. Alanis

    (Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, Mexico)

  • Nancy Arana-Daniel

    (Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, Mexico)

  • Carlos Lopez-Franco

    (Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, Mexico)

Abstract

Mobile robots commonly have to traverse rough terrains. One way to find the easiest traversable path is by determining the types of terrains in the environment. The result of this process can be used by the path planning algorithms to find the best traversable path. In this work, we present an approach for terrain classification from aerial images while using a Convolutional Neural Networks at the pixel level. The segmented images can be used in robot mapping and navigation tasks. The performance of two different Convolutional Neural Networks is analyzed in order to choose the best architecture.

Suggested Citation

  • Gabriel Martinez-Soltero & Alma Y. Alanis & Nancy Arana-Daniel & Carlos Lopez-Franco, 2020. "Semantic Segmentation for Aerial Mapping," Mathematics, MDPI, vol. 8(9), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1456-:d:406298
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