Road Detection With EOSResUNet and Post Vectorizing Algorithm

Oleksandr Filin, Anton Zapara, Serhii Panchenko; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 211-215

Abstract


Object recognition on the satellite images is one of the most relevant and popular topics in the problem of pattern recognition. This was facilitated by many factors, such as a high number of satellites with high-resolution imagery, the significant development of computer vision, especially with a major breakthrough in the field of convolutional neural networks, a wide range of industry verticals for usage and still a quite empty market. Roads are one of the most popular objects for recognition. In this article, we want to present you the combination of work of neural network and postprocessing algorithm, due to which we get not only the coverage mask but also the vectors of all of the individual roads that are present in the image and can be used to address the higher-level tasks in the future. This approach was used to solve the DeepGlobe Road Extraction Challenge

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[bibtex]
@InProceedings{Filin_2018_CVPR_Workshops,
author = {Filin, Oleksandr and Zapara, Anton and Panchenko, Serhii},
title = {Road Detection With EOSResUNet and Post Vectorizing Algorithm},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}