Road Segmentation in Aerial Imagery by Deep Neural Networks with 4-Channel Inputs

Sushma, B.; Fatimah, Binish; Raj, Priyanshu

doi:10.1109/wispnet51692.2021.9419473

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…J. Zhang et al [ 61 ] combined Jaccard and cross entropy losses in the training of the SDG-LinkNet model to avoid the problem of single cross entropy easily falling into local optima. Furthermore, Sushma et al [ 137 ] simultaneously used LZS and boundary loss functions during model training, with results showing their superiority over the mean squared error (MSE) loss.…”

Section: Road Feature Extraction Based On Fully Supervised Deep Learn...mentioning

confidence: 99%

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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Section: Road Feature Extraction Based On Fully Supervised Deep Learn...mentioning

confidence: 99%

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

Mo,

Shi,

Yuan

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Road Segmentation in Aerial Imagery by Deep Neural Networks with 4-Channel Inputs

Cited by 1 publication

References 17 publications

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

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

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