An Attention Augmented Convolution-Based Tiny-Residual UNet for Road Extraction

Patil, Parmeshwar S.; Holambe, Raghunath S.; Waghmare, Laxman M.

doi:10.1109/tai.2024.3357437

Cited by 2 publications

(1 citation statement)

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“…Utilizing the spectral representation of images, Yang et al [52] put forward AFUNet with modulation learning (MoL) for modulating spectral features across different granularities. Patil et al [53] introduced Tiny-AAResUNet, a method that combines the advantages of self-attention mechanisms and the residual UNet architecture to achieve higher accuracy and long-range dependency relationships.…”

Section: Methods Based On Unetmentioning

confidence: 99%

A Review of Deep Learning-Based Methods for Road Extraction from High-Resolution Remote Sensing Images

Liu,

Wu,

et al. 2024

Remote Sensing

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Road extraction from high-resolution remote sensing images has long been a focal and challenging research topic in the field of computer vision. Accurate extraction of road networks holds extensive practical value in various fields, such as urban planning, traffic monitoring, disaster response and environmental monitoring. With rapid development in the field of computational intelligence, particularly breakthroughs in deep learning technology, road extraction technology has made significant progress and innovation. This paper provides a systematic review of deep learning-based methods for road extraction from remote sensing images, focusing on analyzing the application of computational intelligence technologies in improving the precision and efficiency of road extraction. According to the type of annotated data, deep learning-based methods are categorized into fully supervised learning, semi-supervised learning, and unsupervised learning approaches, each further divided into more specific subcategories. They are comparatively analyzed based on their principles, advantages, and limitations. Additionally, this review summarizes the metrics used to evaluate the performance of road extraction models and the high-resolution remote sensing image datasets applied for road extraction. Finally, we discuss the main challenges and prospects for leveraging computational intelligence techniques to enhance the precision, automation, and intelligence of road network extraction.

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Section: Methods Based On Unetmentioning

confidence: 99%