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Delineation of Road Networks from Remote Sensor Data with Deep Learning

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Delineation of Road Networks from Remote Sensor Data with Deep Learning

Xu, Pinjing ORCID: https://orcid.org/0000-0002-3637-6101 (2019) Delineation of Road Networks from Remote Sensor Data with Deep Learning. Masters thesis, Concordia University.

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Abstract

In this thesis we address the problem of semantic segmentation in geospatial data. We investigate different deep neural network architectures and present a complete pipeline for extracting road network vector data from satellite RGB orthophotos of urban areas.

Firstly, we present a network based on the SegNeXt architecture for the semantic segmentation of the roads. A novel loss function is introduced for training the network. The 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 comparisons with other state-of-the-art based on a number of known metrics. This work has been published and presented at the 14 th International Symposium on Visual Computing, 2019.

Finally, we present an altogether different approach to road extraction. We reformulate the task of extracting vectorized road networks as a deep reinforcement learning problem with partially observable state-space and present our preliminary results and future work.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Xu, Pinjing
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:13 November 2019
Thesis Supervisor(s):Poullis, Charalambos
ID Code:986143
Deposited By: Pinjing Xu
Deposited On:26 Jun 2020 13:42
Last Modified:26 Jun 2020 13:42
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