TY - GEN
T1 - Deep Learning Approach to Update Road Network using VGI Data
AU - Manandhar, Prajowal
AU - Marpu, Prashanth
AU - Aung, Zeyar
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/2/14
Y1 - 2019/2/14
N2 - In our earlier work, we worked on extraction of the total width of road by agents traversing in the direction guided by Volunteered Geographic Information (VGI). The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. We make use of the output of our previous work which forms as an input to train the Convolutional Neural Network (CNN). Then, further post processing is performed to remove non-road segments (such as buildings, vegetation, etc) on the output of CNN and finally, obtain the updated road map.
AB - In our earlier work, we worked on extraction of the total width of road by agents traversing in the direction guided by Volunteered Geographic Information (VGI). The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. We make use of the output of our previous work which forms as an input to train the Convolutional Neural Network (CNN). Then, further post processing is performed to remove non-road segments (such as buildings, vegetation, etc) on the output of CNN and finally, obtain the updated road map.
UR - http://www.scopus.com/inward/record.url?scp=85063519713&partnerID=8YFLogxK
U2 - 10.1109/CSPIS.2018.8642728
DO - 10.1109/CSPIS.2018.8642728
M3 - Conference contribution
AN - SCOPUS:85063519713
T3 - 2018 International Conference on Signal Processing and Information Security, ICSPIS 2018
BT - 2018 International Conference on Signal Processing and Information Security, ICSPIS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Signal Processing and Information Security, ICSPIS 2018
Y2 - 7 November 2018 through 8 November 2018
ER -