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Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition)

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 15950

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Editors

Dr. Alessandro Mei

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Institute of Atmospheric Pollution Research (CNR-IIA), National Research Council of Italy, Monterotondo, RM, Italy
Interests: UAV; aircraft and satellite remote sensing; multispectral and hyperspectral remote sensing; imaging spectroscopy; asphalt pavement analysis by remote sensing techniques; analysis of bituminous mixtures by digital imaging processing; characterization of traditional and bio-plastics by hyperspectral devices; photogrammetry and 3D modelling; GIS and geospatial statistics; calibration/validation; land use land cover change; downscaling techniques
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Prof. Dr. Valerio Baiocchi

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Department of Civil Construction and Environmental Engineering, Sapienza University of Rome, 800184 Rome, Italy
Interests: GIS; GNSS; remote sensing; photogrammetry; cultural heritage surveys; drone; civil engineering
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Prof. Dr. Xianfeng Zhang

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Guest Editor

Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China
Interests: hyperspectral and multispectral imagery; quantitative remote sensing; AI applications; road pavement distress assessment; remote sensing for natural disaster assessment
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Special Issue Information

Dear Colleagues,

To reach standard safety conditions, numerous Pavement Management Systems are used for pavement assessment, but don’t allow a rapid and synoptic examination for large road networks. Moreover, due to their need to be calculated from in situ surveys, the acquisition of pavement condition indices is expensive and time consuming. Hence, in the last decade Remote Sensing advancements allow to pursue newest automated or semi-automated procedures for pavement distress detection and analysis. Here because, a great interest has grown-up in the scientific community to the adoption of remote sensed non-invasive techniques in several experimental settings. Remote sensing represents an interesting alternative and challenge for road extraction and pavement aging condition monitoring by using both passive and active satellite sensors.

Here because the aim of this Special Issue is to collect research or review papers focusing on innovative approaches on road distress assessment or extraction using spaceborne/aerial (Remote Sensing) and Unmanned Aerial Vehicles (UAVs) (Proximal Sensing) platforms in different experimental surroundings. Additionally, papers focusing on new approaches related to Near Sensing technologies such as Unmanned Ground Vehicles (UGVs) or field spectroscopy, considered preparators’ for RS analysis, are also welcome.

Moreover, the increase in the adoption of Artificial Intelligence (AI) and Big Data based on remote sensing allows us to manage and share in a more efficient way such huge data frames. Also, geo-statistics can help to improve the knowledge of spectral variability related to pavement distress.

Furthermore, the use of PS techniques shows an increase of their implications on these topics and are frequently related to LIDAR, multi and hyperspectral cameras and field surveys. Such kinds of technologies attain higher outcomes when remote sensed data are correlated to the standardized parameters.

The previous volume of ‘Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms’, was a great success. The aim of this Special Issue is to collect research or review papers focusing on innovative and multidisciplinary approaches on road extraction or distress assessment using spaceborne, aerial and terrestrial platforms in different experimental surroundings.

Dr. Alessandro Mei
Dr. Valerio Baiocchi
Prof. Dr. Xianfeng Zhang
Guest Editors

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Keywords

road extraction and pavement distress analysis
very high resolution satellite imagery
airborne remote sensing
synthetic aperture radar (SAR)
UAV and UGV
multispectral and hyperspectral remote sensing
time series analysis
change detection
imaging spectroscopy
pavement management systems (PMS)
photogrammetry and 3D modelling
GIS modelling for management plan
decision support systems based on remote sensed techniques
geostatistics
artificial intelligence
ML (machine learning)
CNN
deep learning
data fusion
light detection and ranging (LiDAR)
pattern recognition

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Related Special Issue

Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms in Remote Sensing (13 articles)

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Research

20 pages, 3397 KB

Open AccessArticle

GeoMamba: Geometric-Prior-Infused Multi-Scale Deformable Visual Mamba for Crack Semantic Segmentation

by Sangning Li, Bin Liu, Haiyan Guan, Lingfei Ma, Yongtao Yu and Yongming Xu

Remote Sens. 2026, 18(15), 2449; https://doi.org/10.3390/rs18152449 - 24 Jul 2026

Viewed by 413

Abstract

Accurate pavement crack segmentation is critical for road infrastructure assessment, yet it remains challenging due to complex background noise and highly variable crack topologies. While emerging visual Mamba models excel in long-range contextual modeling, their inherent 1D sequence flattening process compromises local 2D spatial continuity. To address this limitation, we propose GeoMamba, a geometric-prior-infused multi-scale deformable visual Mamba network for road crack semantic segmentation. First, we design a Multi-Scale Deformable Visual State Space (MDVSS) module to extract multi-scale contextual features and dynamically adapt to tortuous crack paths through a novel deformable scanning mechanism. Second, a Geometric-Topology Prior Injection (GTPI) module is introduced to mitigate serialization artifacts. By leveraging deterministic, parameter-free analytical operators (i.e., Sobel and Laplace), the GTPI module explicitly extracts and adaptively infuses multi-scale structural priors into the Mamba decoder via gated skip connections, intrinsically reconstructing crack typologies while suppressing pseudo-structural noise. Comprehensive experiments on DeepCrack and Concrete3K datasets demonstrate that GeoMamba outperforms nine state-of-the-art methods. Specifically, it achieves peak performance on the DeepCrack dataset with an mIoU of 83.79% and an F1 score of 89.27%, demonstrating exceptional semantic segmentation performance, superior topological continuity, and robust generalization across diverse pavement materials. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))

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25 pages, 26048 KB

Open AccessArticle

MACER-UNet: A Connected Rural Road Extraction Model Integrating Multi-Scale Perception and Edge Enhancement

by Shaoshuai Tang, Sijia Li, Xingming Zheng and Jianhua Ren

Remote Sens. 2026, 18(11), 1724; https://doi.org/10.3390/rs18111724 - 27 May 2026

Viewed by 465

Abstract

Extracting rural road networks from remote sensing images is crucial for data-driven precision agriculture planning. However, traditional semantic segmentation methods often struggle to achieve both high-precision boundary delineation and topological integrity, especially in heterogeneous rural landscapes. To address these issues, this study proposes MACER-UNet, a novel connectivity-aware road extraction model that integrates multi-scale perception and edge enhancement capabilities. Specifically, MACER-UNet employs ResNet-50 as the backbone network to extract robust deep semantic features. Within the encoder–decoder framework, an atrous spatial pyramid pooling module (ASPP) is embedded to capture rich multi-scale context cues, thereby enhancing robustness to varying road widths and inconsistent imaging conditions. During the decoding process, the convolutional block attention module (CBAM) recalibrates features to reduce noise from the agricultural background. The edge enhancement module (EEM) extracts high-frequency gradient cues for geometric correction and boundary sharpening. This architecture combines spatial attention and edge constraints to balance recognition accuracy and topological connectivity. On the public WHU-CR dataset, MACER-UNet achieved an intersection over union (IoU) of 50.37% and an F1 score of 67.02%, outperforming U-Net (44.27%), DeepLabv3+ (49.43%), and D-LinkNet (49.54%), and its connectivity was comparable to recent state-of-the-art road extraction methods such as C²Net (49.37%) and CGCNet (50.34%). On a self-built dataset with a 3 m resolution in Suihua, the model achieved an IoU of 42.56% and an F1 score of 59.71%. The evaluation results confirm that MACER-UNet provides a road network with geometric consistency and topological integrity for spatial analysis in rural environments. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))

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27 pages, 12820 KB

Open AccessArticle

Positive-Guided Local Supervision for Robust Road Extraction from Remote Sensing Imagery

by Hao He, Shuyang Wang, Lei Huang, Xiaohu Fan, Yongfei Li and Dongfang Yang

Remote Sens. 2026, 18(10), 1589; https://doi.org/10.3390/rs18101589 - 15 May 2026

Viewed by 360

Abstract

Road extraction from high-resolution remote sensing imagery is fundamental to numerous practical applications, yet still faces notable challenges caused by label noise, particularly the underlabeling of rural roads within training datasets. End-to-end dense prediction networks deliver high efficiency and strong global context capture capability, yet they are highly vulnerable to such label noise. In contrast, patch-based methods achieve better robustness but sacrifice global reasoning ability and computational efficiency. This paper proposes a novel training strategy named Positive-guided Local Supervision (PLS), which integrates the strengths of the two aforementioned paradigms. PLS preserves the full end-to-end forward pass to leverage global context, while restricting loss computation to local patches centered on reliably annotated road pixels (positive samples) via a standard dense segmentation loss. By isolating the model from misleading gradients generated in underlabeled regions, PLS effectively mitigates the negative impact of underlabeling without compromising computational efficiency and prediction quality. We evaluate the proposed PLS on two datasets: the public DeepGlobe benchmark and a newly constructed challenging dataset, namely China Four Provinces (CH4P). CH4P includes 13,498 high-resolution images of rural China, which suffers from severe underlabeling inherited from public web maps. Extensive quantitative evaluations on DeepGlobe and the newly built CH4P dataset validate that our PLS strategy surpasses conventional end-to-end baselines and competitive state-of-the-art methods under both noisy original labels and manually refined annotations. On the refined DeepGlobe-mini-test and CH4P-mini-test subsets, PLS obtains prominent absolute IoU improvements of 0.127 and 0.104 over baseline models, respectively, showing distinct superiority in handling severe real-world underlabeling. Qualitative visualizations and cross-dataset generalization tests further demonstrate that PLS can effectively retrieve road segments omitted in raw annotations, delivers strong robustness against practical label noise, and introduces no extra computational burden in the inference stage. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))

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27 pages, 8177 KB

Open AccessArticle

DINOv3-PEFT: A Dual-Branch Collaborative Network with Parameter-Efficient Fine-Tuning for Precise Road Segmentation in SAR Imagery

by Debao Chen, Wanlin Yang, Ye Yuan and Juntao Gu

Remote Sens. 2026, 18(7), 973; https://doi.org/10.3390/rs18070973 - 24 Mar 2026

Viewed by 1474

Abstract

Extracting road networks from Synthetic Aperture Radar (SAR) data represents a core challenge in remote sensing scene analysis, particularly for applications in traffic monitoring and emergency management. The task is complicated by several inherent limitations: speckle noise degrades image quality, geometric distortions arise from the side-looking acquisition geometry, and roads often exhibit weak radiometric separation from surrounding terrain. Traditional processing pipelines and recent single-branch deep learning frameworks have shown insufficient performance when global contextual reasoning and fine-scale spatial detail must both be addressed. This work presents DINOv3-PEFT, a parameter-efficient dual-encoder network designed specifically for SAR road segmentation. The architecture employs two complementary processing streams tailored to SAR characteristics: one stream utilizes adapter-based fine-tuning applied to pre-trained DINOv3 weights (kept frozen), which captures long-distance spatial relationships crucial for maintaining network connectivity despite speckle corruption. The second stream, based on convolutional operations, focuses on extracting localized geometric features that preserve the narrow, elongated structure and sharp boundaries typical of road infrastructure. Feature fusion occurs through the Topological-Geometric Feature Integration (TGFI) Module, which synthesizes multi-scale representations hierarchically. This mechanism proves effective at bridging fragmented road segments and recovering geometric accuracy in scenarios with heavy shadow casting or signal interference. Performance evaluation on the GF-3 satellite dataset across four spatial resolutions (1 m, 3 m, 5 m, and 10 m) demonstrates the proposed method achieves an 82.61% F1-score, a 76.51% IoU, and a 98.08% overall accuracy, all averaged across the four resolutions. When benchmarked against six state-of-the-art methods, DINOv3-PEFT demonstrates substantial improvements in road class segmentation quality and topological connectivity preservation, supporting its robustness for operational SAR road mapping tasks. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))

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24 pages, 4921 KB

Open AccessArticle

YOLOv11-DCFNet: A Robust Dual-Modal Fusion Method for Infrared and Visible Road Crack Detection in Weak- or No-Light Illumination Environments

by Xinbao Chen, Yaohui Zhang, Junqi Lei, Lelin Li, Lifang Liu and Dongshui Zhang

Remote Sens. 2025, 17(20), 3488; https://doi.org/10.3390/rs17203488 - 20 Oct 2025

Cited by 8 | Viewed by 2782

Abstract

Road cracks represent a significant challenge that impacts the long-term performance and safety of transportation infrastructure. Early identification of these cracks is crucial for effective road maintenance management. However, traditional crack recognition methods that rely on visible light images often experience substantial performance degradation in weak-light environments, such as at night or within tunnels. This degradation is characterized by blurred or deficient image textures, indistinct target edges, and reduced detection accuracy, which hinders the ability to achieve reliable all-weather target detection. To address these challenges, this study introduces a dual-modal crack detection method named YOLOv11-DCFNet. This method is based on an enhanced YOLOv11 architecture and incorporates a Cross-Modality Fusion Transformer (CFT) module. It establishes a dual-branch feature extraction structure that utilizes both infrared and visible light within the original YOLOv11 framework, effectively leveraging the high contrast capabilities of thermal infrared images to detect cracks under weak- or no-light conditions. The experimental results demonstrate that the proposed YOLOv11-DCFNet method significantly outperforms the single-modal model (YOLOv11-RGB) in both weak-light and no-light scenarios. Under weak-light conditions, the fusion model effectively utilizes the weak texture features of RGB images alongside the thermal radiation information from infrared (IR) images. This leads to an improvement in Precision from 83.8% to 95.3%, Recall from 81.5% to 90.5%, mAP@0.5 from 84.9% to 92.9%, and mAP@0.5:0.95 from 41.7% to 56.3%, thereby enhancing both detection accuracy and quality. In no-light conditions, the RGB single modality performs poorly due to the absence of visible light information, with an mAP@0.5 of only 67.5%. However, by incorporating IR thermal radiation features, the fusion model enhances Precision, Recall, and mAP@0.5 to 95.3%, 90.5%, and 92.9%, respectively, maintaining high detection accuracy and stability even in extreme no-light environments. The results of this study indicate that YOLOv11-DCFNet exhibits strong robustness and generalization ability across various low illumination conditions, providing effective technical support for night-time road maintenance and crack monitoring systems. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))

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20 pages, 18416 KB

Open AccessArticle

Swin-FSNet: A Frequency-Aware and Spatially Enhanced Network for Unpaved Road Extraction from UAV Remote Sensing Imagery

by Jiwu Guan, Qingzhan Zhao, Wenzhong Tian, Xinxin Yao, Jingyang Li and Wei Li

Remote Sens. 2025, 17(14), 2520; https://doi.org/10.3390/rs17142520 - 20 Jul 2025

Cited by 4 | Viewed by 1805

Abstract

The efficient recognition of unpaved roads from remote sensing (RS) images holds significant value for tasks such as emergency response and route planning in outdoor environments. However, unpaved roads often face challenges such as blurred boundaries, low contrast, complex shapes, and a lack of publicly available datasets. To address these issues, this paper proposes a novel architecture, Swin-FSNet, which combines frequency analysis and spatial enhancement techniques to optimize feature extraction. The architecture consists of two core modules: the Wavelet-Based Feature Decomposer (WBFD) module and the Hybrid Dynamic Snake Block (HyDS-B) module. The WBFD module enhances boundary detection by capturing directional gradient changes at the road edges and extracting high-frequency features, effectively addressing boundary blurring and low contrast. The HyDS-B module, by adaptively adjusting the receptive field, performs spatial modeling for complex-shaped roads, significantly improving adaptability to narrow road curvatures. In this study, the southern mountainous area of Shihezi, Xinjiang, was selected as the study area, and the unpaved road dataset was constructed using high-resolution UAV images. Experimental results on the SHZ unpaved road dataset and the widely used DeepGlobe dataset show that Swin-FSNet performs well in segmentation accuracy and road structure preservation, with an IoU_road of 81.76% and 71.97%, respectively. The experiments validate the excellent performance and robustness of Swin-FSNet in extracting unpaved roads from high-resolution RS images. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))

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20 pages, 6289 KB

Open AccessArticle

A High-Resolution Remote Sensing Road Extraction Method Based on the Coupling of Global Spatial Features and Fourier Domain Features

by Hui Yang, Caili Zhou, Xiaoyu Xing, Yongchuang Wu and Yanlan Wu

Remote Sens. 2024, 16(20), 3896; https://doi.org/10.3390/rs16203896 - 20 Oct 2024

Cited by 12 | Viewed by 3729

Abstract

Remote sensing road extraction based on deep learning is an important method for road extraction. However, in complex remote sensing images, different road information often exhibits varying frequency distributions and texture characteristics, and it is usually difficult to express the comprehensive characteristics of roads effectively from a single spatial domain perspective. To address the aforementioned issues, this article proposes a road extraction method that couples global spatial learning with Fourier frequency domain learning. This method first utilizes a transformer to capture global road features and then applies Fourier transform to separate and enhance high-frequency and low-frequency information. Finally, it integrates spatial and frequency domain features to express road characteristics comprehensively and overcome the effects of intra-class differences and occlusions. Experimental results on HF, MS, and DeepGlobe road datasets show that our method can more comprehensively express road features compared with other deep learning models (e.g., Unet, D-Linknet, DeepLab-v3, DCSwin, SGCN) and extract road boundaries more accurately and coherently. The IOU accuracy of the extracted results also achieved 72.54%, 55.35%, and 71.87%. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))

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19 pages, 5287 KB

Open AccessArticle

GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion

by Yong Wang, Zhenglong He, Xiangqiang Zeng, Juncheng Zeng, Zongxi Cen, Luyang Qiu, Xiaowei Xu and Qunxiong Zhuo

Remote Sens. 2024, 16(10), 1797; https://doi.org/10.3390/rs16101797 - 18 May 2024

Cited by 9 | Viewed by 2933

Abstract

Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone’s perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))

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