Semantic Segmentation for Aerial Mapping

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 appro...

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Bibliographic Details
Main Authors:	Gabriel Martinez-Soltero, Alma Y. Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco
Format:	Article
Language:	English
Published:	MDPI AG 2020-08-01
Series:	Mathematics
Subjects:	mapping semantic segmentation convolutional neural networks unet
Online Access:	https://www.mdpi.com/2227-7390/8/9/1456

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record_format	Article
spelling	doaj-146ebbe6a62b46b79296971996497ff02020-11-25T03:01:11ZengMDPI AGMathematics2227-73902020-08-0181456145610.3390/math8091456Semantic Segmentation for Aerial MappingGabriel Martinez-Soltero0Alma Y. Alanis1Nancy Arana-Daniel2Carlos Lopez-Franco3Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, MexicoCentro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, MexicoCentro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, MexicoCentro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, MexicoMobile 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.https://www.mdpi.com/2227-7390/8/9/1456mappingsemantic segmentationconvolutional neural networksunet
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Gabriel Martinez-Soltero Alma Y. Alanis Nancy Arana-Daniel Carlos Lopez-Franco
spellingShingle	Gabriel Martinez-Soltero Alma Y. Alanis Nancy Arana-Daniel Carlos Lopez-Franco Semantic Segmentation for Aerial Mapping Mathematics mapping semantic segmentation convolutional neural networks unet
author_facet	Gabriel Martinez-Soltero Alma Y. Alanis Nancy Arana-Daniel Carlos Lopez-Franco
author_sort	Gabriel Martinez-Soltero
title	Semantic Segmentation for Aerial Mapping
title_short	Semantic Segmentation for Aerial Mapping
title_full	Semantic Segmentation for Aerial Mapping
title_fullStr	Semantic Segmentation for Aerial Mapping
title_full_unstemmed	Semantic Segmentation for Aerial Mapping
title_sort	semantic segmentation for aerial mapping
publisher	MDPI AG
series	Mathematics
issn	2227-7390
publishDate	2020-08-01
description	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.
topic	mapping semantic segmentation convolutional neural networks unet
url	https://www.mdpi.com/2227-7390/8/9/1456
work_keys_str_mv	AT gabrielmartinezsoltero semanticsegmentationforaerialmapping AT almayalanis semanticsegmentationforaerialmapping AT nancyaranadaniel semanticsegmentationforaerialmapping AT carloslopezfranco semanticsegmentationforaerialmapping
_version_	1724694513101832192

Semantic Segmentation for Aerial Mapping

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