Abstract
In this paper we present a complete pipeline for extracting road network vector data from satellite RGB orthophotos of urban areas. Firstly, a network based on the SegNeXt architecture with a novel loss function is employed for the semantic segmentation of the roads. Results show that the proposed network produces on average better results than other state-of-the-art semantic segmentation techniques. Secondly, we propose a fast post-processing technique for vectorizing the rasterized segmentation result, removing erroneous lines, and refining the road network. The result is a set of vectors representing the road network. We have extensively tested the proposed pipeline and provide quantitative and qualitative comparisons with other state-of-the-art based on a number of known metrics.
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Acknowledgement
This research is based upon work supported by the Natural Sciences and Engineering Research Council of Canada Grants DG-N01670 (Discovery Grant) and DND-N01885 (Collaborative Research and Development with the Department of National Defence Grant).
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Xu, P., Poullis, C. (2019). Delineation of Road Networks Using Deep Residual Neural Networks and Iterative Hough Transform. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_3
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DOI: https://doi.org/10.1007/978-3-030-33720-9_3
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