DenseResSegnet: A Dense Residual Segnet for Road Detection Using Remote Sensing Images

Akhtar, Nadeem; Mandloi, Manish

doi:10.1109/migars57353.2023.10064603

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…To confront challenges like indistinct object boundaries, erroneous classifications, and irregularities, Zhao et al [69] proposed a model called DANet, utilizing two spatial pyramid pooling (ASPP) structures for multi-scale feature fusion. Akhtar et al [70] replaced the basic convolution blocks with dense residual blocks to achieve context information fusion and employ geometric shape analysis to filter out non-road segments after segmentation.…”

Section: Methods Based On Segnetmentioning

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 Segnetmentioning

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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A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

Mo,

Shi,

Yuan

et al. 2024

Sensors

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Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people’s lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided.

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DenseResSegnet: A Dense Residual Segnet for Road Detection Using Remote Sensing Images

Cited by 2 publications

References 21 publications

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

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

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

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