Improving Road Semantic Segmentation Using Generative Adversarial Network
Road network extraction from remotely sensed imagery has become a powerful tool for updating geospatial databases, owing to the success of convolutional neural network (CNN) based deep learning semantic segmentation techniques combined with the high-resolution imagery that modern remote sensing prov...
Saved in:
Main Authors: | Arnick Abdollahi, Biswajeet Pradhan, Gaurav Sharma, Khairul Nizam Abdul Maulud, Abdullah Alamri |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9416669/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Performance and Efficiency Comparison of U-Net and Ghost U-Net in Road Crack Segmentation with Floating Point and Quantization Optimization
by: Haidhi Angkawijana Tedja, et al.
Published: (2024-12-01) -
Ensemble Learning for Three-dimensional Medical Image Segmentation of Organ at Risk in Brachytherapy Using Double U-Net, Bi-directional ConvLSTM U-Net, and Transformer Network
by: Soniya Pal, et al.
Published: (2024-12-01) -
Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI)
by: Arnick Abdollahi, et al.
Published: (2021-07-01) -
MSM-TDE: multi-scale semantics mining and tiny details enhancement network for retinal vessel segmentation
by: Hongbin Zhang, et al.
Published: (2025-01-01) -
Assessing CNN and Semantic Segmentation Models for Coarse Resolution Satellite Image Classification in Subcontinental Scale Land Cover Mapping
by: Tesfaye Adugna, et al.
Published: (2025-01-01)