Review on Active and Passive Remote Sensing Techniques for Road Extraction

Jia, Jianxin; Sun, Hai‐Wei; Jiang, Changhui; Karila, Kirsi; Karjalainen, Mika; Ahokas, Eero; Khoramshahi, Ehsan; Hu, Peilun; Chen, Chen; Xue, Tianru; Wang, Tinghuai; Chen, Yuwei; Hyyppä, Juha

doi:10.3390/rs13214235

Cited by 29 publications

(10 citation statements)

References 236 publications

(250 reference statements)

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“…The data-driven module, based on [ 3 ], also adds a summary of graph-based methods [ 18 ]. Jia et al [ 19 ] discussed the applications of active and passive remote sensing technologies in road extraction, including high-resolution, hyperspectral, synthetic aperture radar (SAR), and airborne laser scanning (ALS) technologies, and also provided a summary of the current state and future prospects of multi-source data fusion. Liu et al [ 20 ] summarized previous data-driven methods as fully supervised learning methods and introduced weakly supervised and unsupervised learning methods.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Simultaneously, objects such as buildings, trees, and clouds can cause varying degrees of obstruction to roads due to different imaging angles and resolutions, resulting in partial information loss or inaccuracies. 3 (2) Efficiency. Large-scale map production and urban planning necessitate higher processing rates and sometimes even demand efficient computations on airborne or satellite platforms, which imposes requirements on the scalability and inference speed of the methods.…”

Section: Introductionmentioning

confidence: 99%

“…Roads exhibit various diversities and complexities in remote sensing images, encompassing different types, such as highways, urban streets, rural paths, intersections, roundabouts, and more. Simultaneously, objects such as buildings, trees, and clouds can cause varying degrees of obstruction to roads due to different imaging angles and resolutions, resulting in partial information loss or inaccuracies 3 . (2) Efficiency.…”

Section: Introductionmentioning

confidence: 99%

DELFormer: detail-enhanced lightweight transformer for road segmentation

Xin,

Fu,

et al. 2023

J. Appl. Rem. Sens.

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.The road segmentation task has become increasingly important in fields such as urban planning, traffic management, and environmental monitoring. However, most existing deep learning-based methods suffer from issues such as poor temporal effectiveness and connectivity, making it a significant challenge to achieve high-precision and high-efficiency road segmentation. We propose a road segmentation model based on a detail-enhanced lightweight transformer. Through the connectivity enhancement module, the issue of spatial information loss is addressed, enhancing the modeling capability of the road network connectivity. The model incorporates a detail-enhancement strategy to capture the relationship between roads and the environment, enhancing the perception and expression of details while maintaining low computational complexity. Furthermore, the use of a lightweight multiple feature fusion module promotes information fusion from features at different scales while a maintaining lightweight design. Extensive experiments on two publicly available datasets demonstrate that our method achieves the best performance in terms of real-time effectiveness and accuracy.

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“…LiDAR point clouds are becoming one of the main data sources for road extraction because of at least four advantages compared with optical imagery. First, the elevation information from LiDAR points can be used to separate elevated structures, such as buildings and vegetation from roads [7,8]. Second, LiDAR points can cover an urban road continuously.…”

Section: Introductionmentioning

confidence: 99%

Extracting Urban Road Footprints from Airborne LiDAR Point Clouds with PointNet++ and Two-Step Post-Processing

Zhang

et al. 2022

Remote Sensing

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In this paper, a novel framework for the automatic extraction of road footprints from airborne LiDAR point clouds in urban areas is proposed. The extraction process consisted of three phases: The first phase is to extract road points by using the deep learning model PointNet++, where the features of the input data include not only those selected from raw LiDAR points, such as 3D coordinate values, intensity, etc., but also the digital number (DN) of co-registered images and generated geometric features to describe a strip-like road. Then, the road points from PointNet++ were post-processed based on graph-cut and constrained triangulation irregular networks, where both the commission and omission errors were greatly reduced. Finally, collinearity and width similarity were proposed to estimate the connection probability of road segments, thereby improving the connectivity and completeness of the road network represented by centerlines. Experiments conducted on the Vaihingen data show that the proposed framework outperformed others in terms of completeness and correctness; in addition, some narrower residential streets with 2 m width, which have normally been neglected by previous studies, were extracted. The completeness and the correctness of the extracted road points were 84.7% and 79.7%, respectively, while the completeness and the correctness of the extracted centerlines were 97.0% and 86.3%, respectively.

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Review on Active and Passive Remote Sensing Techniques for Road Extraction

Cited by 29 publications

References 236 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

DELFormer: detail-enhanced lightweight transformer for road segmentation

Extracting Urban Road Footprints from Airborne LiDAR Point Clouds with PointNet++ and Two-Step Post-Processing

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