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road detection, Monitoring, and Maintenance Using Remotely Sensed Data

REMOTE SENSING 2025年第5期17卷 917-917页

作者： Fiorentini, Nicholas Losa, Massimo Univ Pisa Dept Civil & Ind Engn DICI Largo Lucio Lazzarino 1 I-56122 Pisa Italy

roads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special Issue on "road detection, Monitoring, and Maintenance Using Remotely Sensed Data" presents innovative strategies leveraging remote sensing technologies, artificial intelligence (AI), and non-destructive testing (NDT) to optimize road infrastructure assessment. The ten papers published in this issue explore diverse methodologies, including novel deep learning algorithms for road inventory, novel methods for pavement crack detection, AI-enhanced ground-penetrating radar (GPR) imaging for subsurface assessment, high-resolution optical satellite imagery for unpaved road assessment, and aerial orthophotography for road mapping. Collectively, these studies demonstrate the transformative potential of remotely sensed data for improving the efficiency, accuracy, and scalability of road monitoring and maintenance processes. The findings highlight the importance of integrating multi-source remote sensing data with advanced AI-based techniques to develop cost-effective, automated, and scalable solutions for road authorities. As the first edition of this Special Issue, these contributions lay the groundwork for future advancements in remote sensing applications for road network management.

关键词： road detection road monitoring road maintenance remotely sensed data non-destructive techniques deep learning machine learning crack detection pavement lane markings road inventory aerial orthophotography subsurface imaging ground-penetrating radar satellite imagery image processing road network generation

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road detection in Open-Pit Mining Areas Using D-DMCATNet Network Based on UAV Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Du, Sunwen Chen, Yanwei Feng, Guorui Taiyuan Univ Technol Coll Min Engn Taiyuan 030024 Peoples R China Shanxi Zheda Inst Adv Mat & Chem Engn Taiyuan 030024 Peoples R China

With the growth of the mining industry, efficient resource management and exploration have become key tasks. Traditional methods, relying on manual inspections or satellite remote sensing, are inefficient, costly, and limited by factors such as weather and time. Due to complex terrain and interference from various cover types, traditional road identification methods face challenges. A deep learning method, a double convolution multiscale attention network (D-DMCATNet), is introduced based on the U-Net structure. By combining multiscale convolutional blocks and channel attention mechanisms, the model captures features at different scales and levels of detail. Adaptive attention blocks and dropout mechanisms improve the model's generalization and robustness. First, UAV images of mining roads were annotated to create a training dataset, and then different network architectures and optimizers were compared. The experimental results showed that D-DMCATNet achieved the best performance with an accuracy of 92.92%. Accurate road identification helps managers better plan and monitor resource development and provides data for other land classification tasks. This study is of great significance for mine resource management and exploration.

关键词： roads Accuracy Feature extraction Training Image segmentation Adaptation models Deep learning Robustness Object oriented modeling Data models Double convolution multiscale attention network (D-DMCATNet) network open-pit mining area road detection UAV images

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SkipcrossNets: Adaptive Skip-Cross Fusion for road detection

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AUTOMOTIVE INNOVATION 2025年 1-17页

作者： Gong, Yan Zhang, Xinyu Liu, Hao Jiang, Xinming Li, Zhiwei Gao, Xin Lin, Lei Jin, Dafeng Li, Jun Liu, Huaping Tsinghua Univ Sch Vehicle & Mobil State Key Lab Intelligent Green Vehicle & Mobil Beijing 100084 Peoples R China JD Logist Res Dept X Autonomous Driving Div Beijing 101111 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China Xiamen Univ Sch Informat Dept Artificial Intelligence Xiamen 361005 Peoples R China

Multi-modal fusion is increasingly being used for autonomous driving tasks, as different modalities provide unique information for feature extraction. However, the existing two-stream networks are only fused at a specific network layer, which requires a lot of manual attempts to set up. As the CNN goes deeper, the two modal features become more and more advanced and abstract, and the fusion occurs at the feature level with a large gap, which can easily hurt the performance. To reduce the loss of height and depth information during the process of projecting point clouds into 2D space, calibration parameters are utilized to project the point cloud into Altitude Difference Images (ADIs), which exhibit more distinct road features. In this study, a novel fusion architecture called Skip-cross Networks (SkipcrossNets) is proposed, which adaptively combines ADIs and camera images without being restricted to a specific fusion epoch. Specifically, the skip-cross fusion strategy connects each layer to each layer in a feed-forward manner, and for each layer, the feature maps of all previous layers are used as input and its own feature maps are used as input to all subsequent layers for the other modality, enhancing feature propagation and multi-modal features fusion. This strategy facilitates the selection of the most similar feature layers from two modalities, enhancing feature reuse and providing complementary effects for sparse point cloud features. The advantages of the skip-cross fusion strategy are demonstrated through application to the KITTI and A2D2 datasets, achieving a MaxF score of 96.85% on KITTI and an F1 score of 84.84% on A2D2. The model parameters require only 2.33 MB of memory at a speed of 68.24 FPS, which can be viable for mobile terminals and embedded devices.

关键词： road detection Multi-modal fusion Autonomous driving

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LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free road Space detection

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 652-664页

作者： Khan, Abdullah Aman Shao, Jie Rao, Yunbo She, Lei Shen, Heng Tao Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Sichuan Artificial Intelligence Res Inst Yibin 644000 Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

Humans have long fantasized about self-driving vehicles for the sake of luxury, style, safety, and ease. Free road space detection for collision avoidance and path planning is a vital part of autonomous driving vehicles. Despite many researchers focusing on free road space detection, it remains an open and challenging problem for real-world applications. Many studies have attempted to fuse depth and LiDAR features with visual features to improve the overall performance of free road space detection. However, there is no guideline on how such features should be fused to complement the visual features. Additionally, most of the previously proposed methods are computationally expensive and not suitable for real-life applications. The main motivation of this study is to realize a lightweight model that addresses these problems without compromising performance. As the LiDAR and visual features exist in different spaces, the proposed method attempts to learn various transformation and fusion operations from LiDAR features to complement the visual features. To validate the performance of the proposed method, we conduct comprehensive experiments on prominent benchmark datasets. The results of the experiments reveal the superior performance of the proposed model while being lightweight. LRDNet ranks third overall (with a minor difference) and second among LiDAR-based methods on the KITTI road benchmark dataset. Furthermore, the proposed model is the least computationally expensive among state-of-the-art methods and can be considered an optimal trade-off between speed and accuracy.

关键词： roads Laser radar Visualization Feature extraction Point cloud compression Three-dimensional displays Space vehicles road detection free road space feature pools road segmentation LiDAR KITTI road

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road detection by Using a Generalized Hough Transform

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REMOTE SENSING 2017年第6期9卷 590-590页

作者： Liu, Weifeng Zhang, Zhenqing Li, Shuying Tao, Dapeng China Univ Petr East China Coll Informat & Control Engn Qingdao 266580 Peoples R China China Aerosp Sci & Technol Corp Inst 16 Xian 710100 Peoples R China Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Peoples R China

road detection plays key roles for remote sensing image analytics. Hough transform (HT) is one very typical method for road detection, especially for straight line road detection. Although many variants of Hough transform have been reported, it is still a great challenge to develop a low computational complexity and time-saving Hough transform algorithm. In this paper, we propose a generalized Hough transform (i.e., Radon transform) implementation for road detection in remote sensing images. Specifically, we present a dictionary learning method to approximate the Radon transform. The proposed approximation method treats a Radon transform as a linear transform, which then facilitates parallel implementation of the Radon transform for multiple images. To evaluate the proposed algorithm, we conduct extensive experiments on the popular RSSCN7 database for straight road detection. The experimental results demonstrate that our method is superior to the traditional algorithms in terms of accuracy and computing complexity.

关键词： Hough transform dictionary learning road detection Radon transform

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road detection in dense urban areas using SAR imagery and the usefulness of multiple views

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2002年第11期40卷 2405-2414页

作者： Tupin, F Houshmand, B Datcu, M Telecom Paris Signal & Image Proc Dept F-75634 Paris France CALTECH Jet Prop Lab Pasadena CA 91109 USA IMF DLR D-82234 Wessling Germany

This paper deals with the automatic extraction of the road network in dense urban areas using a few-meters-resolution synthetic aperture radar (SAR) images. The first part presents the proposed method, which is an adaptation of previous work to the specific case of urban areas. The major modifications are 1) the clique potentials of the Markov random field that extracts the road network are adapted and 2) a multiscale framework is used. Results on shuttle mission and aerial SAR images with different resolutions are presented. The second part is dedicated to road extraction combining two SAR images taken with different flight directions (orthogonal and antiparallel passes), and the obtained improvement is analyzed.

关键词： different orientation views Markov random fields road detection SAR images

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road detection From Remote Sensing Images by Generative Adversarial Networks

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IEEE ACCESS 2018年 6卷 25486-25494页

作者： Shi, Qian Liu, Xiaoping Li, Xia Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Guangdong Key Lab Urbanizat & Geosimulat Guangzhou 510275 Guangdong Peoples R China East China Normal Univ Sch Geog Sci Key Lab Geog Informat Sci Minist Educ Shanghai 200241 Peoples R China

road detection with high-precision from very high resolution remote sensing imagery is very important in a huge variety of applications. However, most existing approaches do not automatically extract the road with a smooth appearance and accurate boundaries. To address this problem, we proposed a novel end-to-end generative adversarial network. In particular, we construct a convolutional network based on adversarial training that could discriminate between segmentation maps coming either from the ground truth or generated by the segmentation model. The proposed method could improve the segmentation result by finding and correcting the difference between ground truth and result output by the segmentation model. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods greatly on the performance of segmentation map.

关键词： Generative adversarial networks end-to-end learning road detection

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road detection Based on Illuminant Invariance

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2011年第1期12卷 184-193页

作者： Alvarez, Jose M. Lopez, Antonio M. Univ Autonoma Barcelona Comp Vis Ctr Cerdanyola Del Valles 08193 Spain Univ Autonoma Barcelona Dept Comp Sci Cerdanyola Del Valles 08193 Spain

By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms.

关键词： Driving-assistance system illuminant invariance road detection shadows

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road detection in spaceborne SAR images using a genetic algorithm

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2002年第1期40卷 22-29页

作者： Jeon, BK Jang, JH Hong, KS POSTECH Dept Elect Engn Pohang South Korea

This paper presents a technique for the detection of roads in a spaceborne synthetic aperture radar (SAR) image using a genetic algorithm (GA). roads in a spaceborne SAR image can be modeled as curvilinear structures that possess width. Curve segments, which represent the candidate positions for roads, are extracted from the image using a curvilinear structure detector, and the roads are accurately detected by grouping those curve segments. For this purpose, we designed a grouping method based on a GA, which is a global optimization method. We combined perceptual grouping factors with it and tried to reduce its overall computational cost by introducing a concept of region growing. In this process, a selected initial seed is grown into a finally grouped segment by the iterated GA process, which considers segments only in a search region. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. We applied our method to ERS-1 SAR and SIR-C/X-SAR images that have a resolution of about 30 m. The experimental results show that our method can accurately detect road networks as well as single-track roads and is much faster than a globally applied GA approach.

关键词： genetic algorithm (GA) perceptual grouping road detection synthetic aperture radar (SAR)

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road detection Based on Shearlet for GF-3 Synthetic Aperture Radar Images

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IEEE ACCESS 2020年 8卷 28133-28141页

作者： Sun, Zengguo Lin, Dedao Wei, Wei Wozniak, Marcin Damasevicius, Robertas Minist Educ Key Lab Modern Teaching Technol Xian 710062 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Silesian Tech Univ Inst Math PL-44100 Gliwice Poland Kaunas Univ Technol Fac Informat Multimedia Engn Dept LT-44249 Kaunas Lithuania

GF-3 satellite is China's first C-band multi-polarized synthetic aperture radar (SAR) satellite with the 1-meter resolution, which has been widely used in various fields. road detection for GF-3 SAR images is an important part of the application of GF-3, especially in fields of map update, target recognition, and image matching. However, speckle appears in GF-3 SAR images due to coherent imaging system and it hinders the interpretation of images seriously. Especially the detection of weak roads under strong speckle background becomes extremely difficult. As a representative of multiscale geometric analysis (MGA) tool, shearlet has the optimal sparse representation feature and strong directional orientation, which can effectively capture edge and other anisotropic feature information, and can accurately describe the sparse characteristics of GF-3 SAR images. Based on shearlet, a method for detecting weak roads under strong speckle interference is proposed. Firstly, the Frost filter is used for despeckling. Secondly, shearlet is used for road detection. Finally, morphological operations are adopted to obtain the final result. road detection experiments on various types of GF-3 SAR images demonstrate that, the proposed method can effectively overcome the interference of speckle, and completely and smoothly detect road information, which is very suitable for the detection of weak roads under strong speckle interference of GF-3 SAR images.

关键词： GF-3 synthetic aperture radar images road detection shearlet morphological operation

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