Authors :
Bayu Yanuargi; Ema Utami; Kusnawi
Volume/Issue :
Volume 7 - 2022, Issue 12 - December
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3WuVHh8
DOI :
https://doi.org/10.5281/zenodo.7487943
Abstract :
Road network data is critical information that
used by government for development planning and by the
services provider such as transportation and logistic
company to deliver their services and prices calculations.
Use of sentinel 2A satellite imagery data will solve the
road map update cost issue since this data is free to use.
The only problem on sentinel 2A is only about the medial
spatial resolution that only able to detect 10 meters object.
Using GPS data for the ground truth data will help to
create the road masking data that can be used for the
training. The result of the combination between these two
data on Convolutional Neural Network are satisfied
enough with accuracies score is 99% and soft dice error
only 0.5%.
Keywords :
Convolutional Neural Network (CNN), Sentinel, GPS, U-Net, Deep Learning.
Road network data is critical information that
used by government for development planning and by the
services provider such as transportation and logistic
company to deliver their services and prices calculations.
Use of sentinel 2A satellite imagery data will solve the
road map update cost issue since this data is free to use.
The only problem on sentinel 2A is only about the medial
spatial resolution that only able to detect 10 meters object.
Using GPS data for the ground truth data will help to
create the road masking data that can be used for the
training. The result of the combination between these two
data on Convolutional Neural Network are satisfied
enough with accuracies score is 99% and soft dice error
only 0.5%.
Keywords :
Convolutional Neural Network (CNN), Sentinel, GPS, U-Net, Deep Learning.
