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Title: USABILITY EVALUATION OF CONVOLUTIONAL NEURAL NETWORKS IN ROADS EXTRACTION FROM AERIAL IMAGES
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USABILITY EVALUATION OF CONVOLUTIONAL NEURAL NETWORKS IN ROADS EXTRACTION FROM AERIAL IMAGES
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1314-2704
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English
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The main source of information needed to build, update and maintain spatial databases are aerial images. The biggest advance of aerial images is their capability to efficiently cover large areas. With the continuous development of technology, aerial imaging has become cheaper and more affordable than any time before. With this advancement of technology, large amounts of data need to be processed. In order to extract information from aerial images, they need to be processed into ortho-rectified images and then vectorized. The process of vectorization (extracting information from images and creating spatial structures) is mainly done by human operator. With the amount of the data and demand for short processing period in order to guarantee information recency, this process needs to be automated. Neural networks have proven their great usability in wide range of applications varying from stock estimation to autonomously driven vehicles. Convolutional neural networks create set of neural networks with specialization on computer vision. Their ability to correctly distinguish between multiple object presented in the images have been evaluated. We designed architecture of convolutional neural network that is suitable for road extraction from aerial images. The proposed architecture was tested in the area of city Pie??any, Slovakia. The task designed for convolutional neural network is to recognize roads in the aerial images and label pixels that are parts of the road segments. The accuracy over 85% achieved during the experiment indicates great potential in using convolutional neural networks in objects extraction from aerial images.
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conference
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17th International Multidisciplinary Scientific GeoConference SGEM 2017
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17th International Multidisciplinary Scientific GeoConference SGEM 2017, 29 June - 5 July, 2017
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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1105-1112
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29 June - 5 July, 2017
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website
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cdrom
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3068
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roads detection; neural networks; remote sensing; computer vision
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