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Towards Automatic Extraction and Updating of VGI-Based Road Networks Using Deep Learning

Department of Electrical Engineering and Computer Science, Khalifa University, Masdar City, P.O. Box 54224, Abu Dhabi, UAE
Department of Information Engineering and Computer Science, University of Trento, via Sommarive, 9, 38123 Trento, Italy
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1012;
Received: 18 March 2019 / Revised: 15 April 2019 / Accepted: 24 April 2019 / Published: 28 April 2019
(This article belongs to the Section Remote Sensing Image Processing)
This work presents an approach to road network extraction in remote sensing images. In our earlier work, we worked on the extraction of the road network using a multi-agent approach guided by Volunteered Geographic Information (VGI). The limitation of this VGI-only approach is its inability to update the new road developments as it only follows the VGI. In this work, we employ a deep learning approach to update the road network to include new road developments not captured by the existing VGI. The output of the first stage is used to train a Convolutional Neural Network (CNN) in the second stage to generate a general model to classify road pixels. Post-processing is used to correct the undesired artifacts such as buildings, vegetation, occlusions, etc. to generate a final road map. Our proposed method is tested on the satellite images acquired over Abu Dhabi, United Arab Emirates and the aerial images acquired over Massachusetts, United States of America, and is observed to produce accurate results. View Full-Text
Keywords: road extraction; road updating; VGI; CNN; segment connection road extraction; road updating; VGI; CNN; segment connection
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Manandhar, P.; Marpu, P.R.; Aung, Z.; Melgani, F. Towards Automatic Extraction and Updating of VGI-Based Road Networks Using Deep Learning. Remote Sens. 2019, 11, 1012.

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