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Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images

Ghandorh, Hamza; Boulila, Wadii; Masood, Sharjeel; Koubaa, Anis; Ahmed, Fawad; Ahmad, Jawad


Hamza Ghandorh

Wadii Boulila

Sharjeel Masood

Anis Koubaa

Fawad Ahmed


Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development in detecting roads in high-resolution (HR) satellite images based on semantic segmentation. However, the objects being segmented in such images are of small size, and not all the information in the images is equally important when making a decision. This paper proposes a novel approach to road detection based on semantic segmentation and edge detection. Our approach aims to combine these two techniques to improve road detection, and it produces sharp-pixel segmentation maps, using the segmented masks to generate road edges. In addition, some well-known architectures, such as SegNet, used multi-scale features without refinement; thus, using attention blocks in the encoder to predict fine segmentation masks resulted in finer edges. A combination of weighted cross-entropy loss and the focal Tversky loss as the loss function is also used to deal with the highly imbalanced dataset. We conducted various experiments on two datasets describing real-world datasets covering the three largest regions in Saudi Arabia and Massachusetts. The results demonstrated that the proposed method of encoding HR feature maps effectively predicts sharp segmentation masks to facilitate accurate edge detection, even against a harsh and complicated background.


Ghandorh, H., Boulila, W., Masood, S., Koubaa, A., Ahmed, F., & Ahmad, J. (2022). Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images. Remote Sensing, 14(3), Article 613.

Journal Article Type Article
Acceptance Date Jan 25, 2022
Online Publication Date Jan 27, 2022
Publication Date 2022
Deposit Date Jan 31, 2022
Publicly Available Date Jan 31, 2022
Journal Remote Sensing
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 14
Issue 3
Article Number 613
Keywords deep learning; convolutional neural networks; 2D attention; satellite images; road segmentation; edge detection
Public URL


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