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Journal articles on the topic 'Road feature extraction'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

He, Haiqing, Yan Wei, Fuyang Zhou, and Hai Zhang. "A Deep Neural Network for Road Extraction with the Capability to Remove Foreign Objects with Similar Spectra." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 193–99. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-193-2024.

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Abstract. Existing road extraction methods based on deep learning often struggle with distinguishing ground objects that share similar spectral information, such as roads and buildings. Consequently, this study proposes a dual encoder-decoder deep neural network to address road extraction in complex backgrounds. In the feature extraction stage, the first encoder-decoder designed for extracting road features. The second encoder-decoder utilized for extracting building features. During the feature fusion stage, road features and building features are integrated using a subtraction method. The re

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Zhong, Bo, Hongfeng Dan, MingHao Liu, et al. "FERDNet: High-Resolution Remote Sensing Road Extraction Network Based on Feature Enhancement of Road Directionality." Remote Sensing 17, no. 3 (2025): 376. https://doi.org/10.3390/rs17030376.

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The identification of roads from satellite imagery plays an important role in urban design, geographic referencing, vehicle navigation, geospatial data integration, and intelligent transportation systems. The use of deep learning methods has demonstrated significant advantages in the extraction of roads from remote sensing data. However, many previous deep learning-based road extraction studies overlook the connectivity and completeness of roads. To address this issue, this paper proposes a new high-resolution satellite road extraction network called FERDNet. In this paper, to effectively dist

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Kumar Reddy, Sama Lenin, C. V. Rao, P. Rajesh Kumar, R. V. G. Anjaneyulu, and B. Gopala Krishna. "An index based road feature extraction from LANDSAT-8 OLI images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1319. http://dx.doi.org/10.11591/ijece.v11i2.pp1319-1336.

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Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (

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Sama, Lenin Kumar Reddy, V. Rao C., Rajesh Kumar P., V. G. Anjaneyulu R., and Gopala Krishna B. "An index based road feature extraction from LANDSAT-8 OLI images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1319–36. https://doi.org/10.11591/ijece.v11i2.pp1319-1336.

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Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (Top-hat or Bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (

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Feng, Dejun, Xingyu Shen, Yakun Xie, Yangge Liu, and Jian Wang. "Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery." Remote Sensing 13, no. 24 (2021): 4974. http://dx.doi.org/10.3390/rs13244974.

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Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain anal

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Chen, Jie, Libo Yang, Hao Wang, et al. "Road Extraction from High-Resolution Remote Sensing Images via Local and Global Context Reasoning." Remote Sensing 15, no. 17 (2023): 4177. http://dx.doi.org/10.3390/rs15174177.

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Road extraction from high-resolution remote sensing images is a critical task in image understanding and analysis, yet it poses significant challenges because of road occlusions caused by vegetation, buildings, and shadows. Deep convolutional neural networks have emerged as the leading approach for road extraction because of their exceptional feature representation capabilities. However, existing methods often yield incomplete and disjointed road extraction results. To address this issue, we propose CR-HR-RoadNet, a novel high-resolution road extraction network that incorporates local and glob

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Geng, Kai, Xian Sun, Zhiyuan Yan, Wenhui Diao, and Xin Gao. "Topological Space Knowledge Distillation for Compact Road Extraction in Optical Remote Sensing Images." Remote Sensing 12, no. 19 (2020): 3175. http://dx.doi.org/10.3390/rs12193175.

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Road extraction from optical remote sensing images has drawn much attention in recent decades and has a wide range of applications. Most of the previous studies rarely take into account the unique topological characteristics of the road. It is the most apparent feature of linear structure that describes the variety of connection relationships of the road. However, designing a particular topological feature extraction network usually results in a model that is too heavy and impractical. To address the problems mentioned above, in this paper, we propose a lightweight topological space network fo

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Li, Y., X. Hu, H. Guan, and P. Liu. "AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON MULTIPLE FEATURES FROM LiDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 289–93. http://dx.doi.org/10.5194/isprs-archives-xli-b3-289-2016.

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The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1)

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Li, Y., X. Hu, H. Guan, and P. Liu. "AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON MULTIPLE FEATURES FROM LiDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 289–93. http://dx.doi.org/10.5194/isprsarchives-xli-b3-289-2016.

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The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1)

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Liu, Bohua, Jianli Ding, Jie Zou, Jinjie Wang, and Shuai Huang. "LDANet: A Lightweight Dynamic Addition Network for Rural Road Extraction from Remote Sensing Images." Remote Sensing 15, no. 7 (2023): 1829. http://dx.doi.org/10.3390/rs15071829.

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Automatic road extraction from remote sensing images has an important impact on road maintenance and land management. While significant deep-learning-based approaches have been developed in recent years, achieving a suitable trade-off between extraction accuracy, inference speed and model size remains a fundamental and challenging issue for real-time road extraction applications, especially for rural roads. For this purpose, we developed a lightweight dynamic addition network (LDANet) to exploit rural road extraction. Specifically, considering the narrow, complex and diverse nature of rural ro

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Lenin Kumar Reddy, Sama, C. V. Rao, and P. Rajesh Kumar. "Road Feature Extraction from LANDSAT-8 and ResourceSat-2 Images." Russian Journal of Earth Sciences 21, no. 3 (2021): 1–9. http://dx.doi.org/10.2205/2021es000772.

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This paper presents a methodology of road feature extraction from the different resolutions of Remote Sensing images of Landsat-8 Operational Lander Image (OLI) and ResourceSat-2 of Linear Imaging Self Sensor-3 (LISS-3) and LISS-4 sensors with the spatial resolutions of 15 m, 24 m, and 5 m. In the methodology of road extraction, an index is proposed based on the spectral profile of Roads, also involving Morphological transform (Top-Hat or Bot-Hat) and Markov Random Fields (MRF). In the proposed index, Short Wave Infrared (SWIR) band has a significant role in the detection of roads from sensors

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Jie, Yongshi, Hongyan He, Kun Xing, et al. "MECA-Net: A MultiScale Feature Encoding and Long-Range Context-Aware Network for Road Extraction from Remote Sensing Images." Remote Sensing 14, no. 21 (2022): 5342. http://dx.doi.org/10.3390/rs14215342.

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Road extraction from remote sensing images is significant for urban planning, intelligent transportation, and vehicle navigation. However, it is challenging to automatically extract roads from remote sensing images because the scale difference of roads in remote sensing images varies greatly, and slender roads are difficult to identify. Moreover, the road in the image is often blocked by the shadows of trees and buildings, which results in discontinuous and incomplete extraction results. To solve the above problems, this paper proposes a multiscale feature encoding and long-range context-aware

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Zhao, Like, Linfeng Ye, Mi Zhang, Huawei Jiang, Zhen Yang, and Mingwang Yang. "DPSDA-Net: Dual-Path Convolutional Neural Network with Strip Dilated Attention Module for Road Extraction from High-Resolution Remote Sensing Images." Remote Sensing 15, no. 15 (2023): 3741. http://dx.doi.org/10.3390/rs15153741.

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Roads extracted from high-resolution remote sensing images are widely used in many fields, such as autonomous driving, road planning, disaster relief, etc. However, road extraction from high-resolution remote sensing images has certain deficiencies in connectivity and completeness due to obstruction by surrounding ground objects, the influence of similar targets, and the slender structure of roads themselves. To address this issue, we propose a novel dual-path convolutional neural network with a strip dilated attention module, named DPSDA-Net, which adopts a U-shaped encoder–decoder structure,

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Liu, Ziwei, Mingchang Wang, Fengyan Wang, and Xue Ji. "A Residual Attention and Local Context-Aware Network for Road Extraction from High-Resolution Remote Sensing Imagery." Remote Sensing 13, no. 24 (2021): 4958. http://dx.doi.org/10.3390/rs13244958.

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Extracting road information from high-resolution remote sensing images (HRI) can provide crucial geographic information for many applications. With the improvement of remote sensing image resolution, the image data contain more abundant feature information. However, this phenomenon also enhances the spatial heterogeneity between different types of roads, making it difficult to accurately discern the road and non-road regions using only spectral characteristics. To remedy the above issues, a novel residual attention and local context-aware network (RALC-Net) is proposed for extracting a complet

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Ren, Yongfeng, Yongtao Yu, and Haiyan Guan. "DA-CapsUNet: A Dual-Attention Capsule U-Net for Road Extraction from Remote Sensing Imagery." Remote Sensing 12, no. 18 (2020): 2866. http://dx.doi.org/10.3390/rs12182866.

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The up-to-date and information-accurate road database plays a significant role in many applications. Recently, with the improvement in image resolutions and quality, remote sensing images have provided an important data source for road extraction tasks. However, due to the topology variations, spectral diversities, and complex scenarios, it is still challenging to realize fully automated and highly accurate road extractions from remote sensing images. This paper proposes a novel dual-attention capsule U-Net (DA-CapsUNet) for road region extraction by combining the advantageous properties of ca

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Zhao, H. H., and H. Y. Guan. "MULTI-FEATURE-MARKS BASED INFORMATION EXTRACTION OF URBAN GREEN SPACE ALONG ROAD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2353–57. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2353-2018.

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Green space along road of QuickBird image was studied in this paper based on multi-feature-marks in frequency domain. The magnitude spectrum of green along road was analysed, and the recognition marks of the tonal feature, contour feature and the road were built up by the distribution of frequency channels. Gabor filters in frequency domain were used to detect the features based on the recognition marks built up. The detected features were combined as the multi-feature-marks, and watershed based image segmentation were conducted to complete the extraction of green space along roads. The segmen

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Nie, Yunfeng, Kang An, Xingfeng Chen, et al. "An Improved U-Net Network for Sandy Road Extraction from Remote Sensing Imagery." Remote Sensing 15, no. 20 (2023): 4899. http://dx.doi.org/10.3390/rs15204899.

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The extraction of sandy roads from remote sensing images is important for field ecological patrols and path planning. Extraction studies on sandy roads face limitations because of various factors (e.g., sandy roads may have poor continuity, may be obscured by external objects, and/or have multi-scale and banding characteristics), in addition to the absence of publicly available datasets. Accordingly, in this study, we propose using the remote sensing imagery of a sandy road (RSISR) dataset and design a sandy road extraction model (Parallel Attention Mechanism-Unet, or PAM-Unet) based on Gaofen

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Xie, Yan, Fang Miao, Kai Zhou, and Jing Peng. "HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information." ISPRS International Journal of Geo-Information 8, no. 12 (2019): 571. http://dx.doi.org/10.3390/ijgi8120571.

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Road extraction is a unique and difficult problem in the field of semantic segmentation because roads have attributes such as slenderness, long span, complexity, and topological connectivity, etc. Therefore, we propose a novel road extraction network, abbreviated HsgNet, based on high-order spatial information global perception network using bilinear pooling. HsgNet, taking the efficient LinkNet as its basic architecture, embeds a Middle Block between the Encoder and Decoder. The Middle Block learns to preserve global-context semantic information, long-distance spatial information and relation

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Lang, Hong, Yuan Peng, Zheng Zou, Shengxue Zhu, Yichuan Peng, and Hao Du. "Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds." Sensors 24, no. 20 (2024): 6544. http://dx.doi.org/10.3390/s24206544.

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Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is propose

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Guan, Jiwu, Qingzhan Zhao, Wenzhong Tian, Xinxin Yao, Jingyang Li, and Wei Li. "Swin-FSNet: A Frequency-Aware and Spatially Enhanced Network for Unpaved Road Extraction from UAV Remote Sensing Imagery." Remote Sensing 17, no. 14 (2025): 2520. https://doi.org/10.3390/rs17142520.

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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) mod

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Wu, H., Z. Xie, C. Wen, C. Wang, and J. Li. "ON-ROAD INFORMATION EXTRACTION FROM LIDAR DATA VIA MULTIPLE FEATURE MAPS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020 (August 3, 2020): 207–13. http://dx.doi.org/10.5194/isprs-annals-v-1-2020-207-2020.

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Abstract. On-road information, including road boundaries, road markings, and road cracks, provides significant guidance or warning to all road users. Recently, the on-road information extraction from LiDAR data have been widely studied. However, for the LiDAR data with lower accuracy and higher noise, some detailed information, such as road boundary, is difficult to be extracted correctly. Furthermore, most of previous studies lack an exploration of efficiently extracting multiple on-road information from a single framework. In this paper, we propose a new framework that can simultaneously ext

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Yin, Anchao, Chao Ren, Zhiheng Yan, et al. "HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion." Applied Sciences 13, no. 14 (2023): 8237. http://dx.doi.org/10.3390/app13148237.

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Road extraction from high-resolution satellite images has become a significant focus in the field of remote sensing image analysis. However, factors such as shadow occlusion and spectral confusion hinder the accuracy and consistency of road extraction in satellite images. To overcome these challenges, this paper presents a multi-scale fusion-based road extraction framework, HRU-Net, which exploits the various scales and resolutions of image features generated during the encoding and decoding processes. First, during the encoding phase, we develop a multi-scale feature fusion module with upsamp

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Ural, S., J. Shan, M. A. Romero, and A. Tarko. "ROAD AND ROADSIDE FEATURE EXTRACTION USING IMAGERY AND LIDAR DATA FOR TRANSPORTATION OPERATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 239–46. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-239-2015.

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Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various

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Ding, Cheng, Liguo Weng, Min Xia, and Haifeng Lin. "Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image." ISPRS International Journal of Geo-Information 10, no. 4 (2021): 245. http://dx.doi.org/10.3390/ijgi10040245.

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Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction. In order to solve these problem, this paper proposes a Non-Local Feat

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Mahara, Arpan, Md Rezaul Karim Khan, Liangdong Deng, Naphtali Rishe, Wenjia Wang, and Seyed Masoud Sadjadi. "Automated Road Extraction from Satellite Imagery Integrating Dense Depthwise Dilated Separable Spatial Pyramid Pooling with DeepLabV3+." Applied Sciences 15, no. 3 (2025): 1027. https://doi.org/10.3390/app15031027.

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Road extraction is a sub-domain of remote sensing applications; it is a subject of extensive and ongoing research. The procedure of automatically extracting roads from satellite imagery encounters significant challenges due to the multi-scale and diverse structures of roads; improvement in this field is needed. Convolutional neural networks (CNNs), especially the DeepLab series known for its proficiency in semantic segmentation due to its efficiency in interpreting multi-scale objects’ features, address some of these challenges caused by the varying nature of roads. The present work proposes t

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Sun, Chenyang, Yang Wang, Yanfei Deng, Huafu Li, and Junqi Guo. "Research on Vehicle Re-identification for Vehicle Road Collaboration." Journal of Physics: Conference Series 2456, no. 1 (2023): 012025. http://dx.doi.org/10.1088/1742-6596/2456/1/012025.

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Abstract Vehicles and roads cooperate to perceive traffic targets, which can reduce the perception blind spots of vehicles and improve driving safety. In this paper, we proposes a vehicle re-identification method oriented to vehicle-road coordination. This method first designs a lightweight vehicle re-identification network based on ShufflenetV2 to solve the computational efficiency problem of vehicle-road coordination scenarios, which can efficiently complete vehicle feature extraction; then, due to the real-time requirements of scenario communication, an adaptive feature conversion mechanism

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Wei, Zhiheng, and Zhenyu Zhang. "Remote Sensing Image Road Extraction Network Based on MSPFE-Net." Electronics 12, no. 7 (2023): 1713. http://dx.doi.org/10.3390/electronics12071713.

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Road extraction is a hot task in the field of remote sensing, and it has been widely concerned and applied by researchers, especially using deep learning methods. However, many models using convolutional neural networks ignore the attributes of roads, and the shape of the road is banded and discrete. In addition, the continuity and accuracy of road extraction are also affected by narrow roads and roads blocked by trees. This paper designs a network (MSPFE-Net) based on multi-level strip pooling and feature enhancement. The overall architecture of MSPFE-Net is encoder-decoder, and this network

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Chen, Guang, Hai Gang Sui, Liang Dong, and Hua Sun. "Semi-Automatic Extraction Method for Low Contrast Road Based on Gabor Filter and Simulated Annealing." Advanced Materials Research 989-994 (July 2014): 3644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3644.

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High-resolution satellite remote sensing image are mostly used for accurate updating of GIS data. As the primary GIS data, urban roads on the image show the rich geometric features and radiation characteristics, that edge detection and grouping becoming an important way to solve the road extraction. However, edge elements obtained from images are always discontinuous for interference of noise and weak contrast between road and background. What more, vehicles, plant, buildings and shadow blocking results in weak grouping relation of elements. In processing, insignificant candidate road may be w

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Zhang, Zixuan, Xuan Sun, and Yuxi Liu. "GMR-Net: Road-Extraction Network Based on Fusion of Local and Global Information." Remote Sensing 14, no. 21 (2022): 5476. http://dx.doi.org/10.3390/rs14215476.

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Road extraction from high-resolution remote-sensing images has high application values in various fields. However, such work is susceptible to the influence of the surrounding environment due to the diverse slenderness and complex connectivity of roads, leading to false judgment and omission during extraction. To solve this problem, a road-extraction network, the global attention multi-path dilated convolution gated refinement Network (GMR-Net), is proposed. The GMR-Net is facilitated by both local and global information. A residual module with an attention mechanism is first designed to obtai

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Jia, Yuanxin, Xining Zhang, Ru Xiang, and Yong Ge. "Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network." Remote Sensing 15, no. 17 (2023): 4193. http://dx.doi.org/10.3390/rs15174193.

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With the development of agricultural and rural modernization, the informatization of rural roads has been an inevitable requirement for promoting rural revitalization. To date, however, the vast majority of road extraction methods mainly focus on urban areas and rely on very high-resolution satellite or aerial images, whose costs are not yet affordable for large-scale rural areas. Therefore, a deep learning (DL)-based super-resolution mapping (SRM) method has been considered to relieve this dilemma by using freely available Sentinel-2 imagery. However, few DL-based SRM methods are suitable due

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Gui, Linger, Xingjian Gu, Fen Huang, Shougang Ren, Huanhuan Qin, and Chengcheng Fan. "Road Extraction from Remote Sensing Images Using a Skip-Connected Parallel CNN-Transformer Encoder-Decoder Model." Applied Sciences 15, no. 3 (2025): 1427. https://doi.org/10.3390/app15031427.

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Extracting roads from remote sensing images holds significant practical value across fields like urban planning, traffic management, and disaster monitoring. Current Convolutional Neural Network (CNN) methods, praised for their robust local feature learning enabled by inductive biases, deliver impressive results. However, they face challenges in capturing global context and accurately extracting the linear features of roads due to their localized receptive fields. To address these shortcomings of traditional methods, this paper proposes a novel parallel encoder architecture that integrates a C

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Zhou, Guoyuan, Changxian He, Hao Wang, et al. "RIRNet: A Direction-Guided Post-Processing Network for Road Information Reasoning." Remote Sensing 16, no. 14 (2024): 2666. http://dx.doi.org/10.3390/rs16142666.

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Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, and shadows in HRSIs, resulting in incomplete and discontinuous road extraction results. To address this issue, we propose a lightweight post-processing network called RIRNet in this study, which include an information inference module and a road direction inference task branch. The informa

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O’Brien, Douglas. "Road network extraction from spot panchromatic data." CISM journal 43, no. 2 (1989): 121–26. http://dx.doi.org/10.1139/geomat-1989-0011.

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The efficient revision of cartographic data bases using digital imagery implies some form of feature extraction. At present classification techniques can be used to extract certain area outlines, but the automatic detection of linear features such as roads is difficult. One possible method of extracting these features is through mathematical morphology. Mathematical morphology is a form of image treatment that has been applied to remote sensing in recent years. It is possible to extract a binary representation of the road network in an image using this approach, and then treat the results to r

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Yuslena, Sari, Budi Prakoso Puguh, and Rizky Baskara Andreyan. "Application of neural network method for road crack detection." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 1962–67. https://doi.org/10.12928/TELKOMNIKA.v18i4.14825.

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The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in

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Wang, Shuyang, Xiaodong Mu, Dongfang Yang, Hao He, and Peng Zhao. "Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields." Remote Sensing 13, no. 3 (2021): 465. http://dx.doi.org/10.3390/rs13030465.

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Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging task due to complex circumstances and factors such as occlusion. To improve the accuracy and connectivity of road extraction, we propose an inner convolution integrated encoder-decoder network with the post-processing of directional conditional random fields. Firstly, we design an inner convolutional network which can propagate information slice-by-sl

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Zhou, Tingting, Chenglin Sun, and Haoyang Fu. "Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction." Remote Sensing 11, no. 1 (2019): 79. http://dx.doi.org/10.3390/rs11010079.

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Traditional road extraction algorithms, which focus on improving the accuracy of road surfaces, cannot overcome the interference of shelter caused by vegetation, buildings, and shadows. In this paper, we extract the roads via road centerline extraction, road width extraction, broken centerline connection, and road reconstruction. We use a multiscale segmentation algorithm to segment the images, and feature extraction to get the initial road. The fast marching method (FMM) algorithm is employed to obtain the boundary distance field and the source distance field, and the branch backing-tracking

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Cabral, Frederico Soares, Hidekazu Fukai, and Satoshi Tamura. "Feature Extraction Methods Proposed for Speech Recognition Are Effective on Road Condition Monitoring Using Smartphone Inertial Sensors." Sensors 19, no. 16 (2019): 3481. http://dx.doi.org/10.3390/s19163481.

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The objective of our project is to develop an automatic survey system for road condition monitoring using smartphone devices. One of the main tasks of our project is the classification of paved and unpaved roads. Assuming recordings will be archived by using various types of vehicle suspension system and speeds in practice, hence, we use the multiple sensors found in smartphones and state-of-the-art machine learning techniques for signal processing. Despite usually not being paid much attention, the results of the classification are dependent on the feature extraction step. Therefore, we have

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Dharani Sri, Vuyyuru. "Road Extraction from Remote Sensing Images Using a Skip-Connected Parallel CNN-Transformer Encoder-Decoder Model." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49159.

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Abstract - Extracting roads from remote sensing images holds significant practical value across fields like urban planning, traffic management, and disaster monitoring. Current Convolutional Neural Network (CNN) methods, praised for their robust local feature learning enabled by inductive biases, deliver impressive results. However, they face challenges in capturing global context and accurately extracting the linear features of roads due to their localized receptive fields. To address these shortcomings of traditional methods, this paper proposes a novel parallel encoder architecture that int

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Yunhong, Li, Wang Mei, Su Xueping, Li Limin, Zhang Fuxing, and Hao Teji. "Road extraction from remote sensing images by combining attention and context fusion." Insights of Automation in Manufacturing 1, no. 1 (2024): 32–41. http://dx.doi.org/10.59782/iam.v1i1.205.

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Aiming at the problem that the objects in remote sensing images are complex, and the roads are long, thin, continuously distributed and easily blocked, a road extraction model for remote sensing images combining attention and context fusion (ACFD-LinkNet) is proposed. The model is based on the D-LinkNet network. First, a strip attention module is used after the last convolutional layer of the D-LinkNet network encoder to enhance the feature extraction ability of roads of different scales, better capture the global features of the road, and capture the long-distance information of the road; sec

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Previtali, M., L. Barazzetti, and M. Scaioni. "AUTOMATED ROAD INFORMATION EXTRACTION FROM HIGH RESOLUTION AERIAL LIDAR DATA FOR SMART ROAD APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 533–39. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-533-2020.

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Abstract. Automatic extraction of road features from LiDAR data is a fundamental task for different applications, including asset management. The availability of updated and reliable models is even more important in the context of smart roads. One of the main advantages of LiDAR data compared with other sensing instruments is the possibility to directly get 3D information. However, the task of deriving road networks form LiDAR data acquired with Airborne Laser Scanning (ALS) may be quite complex due to occlusions, low feature separability and shadowing from contextual objects. Indeed, even if

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Xu, Chuan, Qi Zhang, Liye Mei, et al. "Dense Multiscale Feature Learning Transformer Embedding Cross-Shaped Attention for Road Damage Detection." Electronics 12, no. 4 (2023): 898. http://dx.doi.org/10.3390/electronics12040898.

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Road damage detection is essential to the maintenance and management of roads. The morphological road damage contains a large number of multi-scale features, which means that existing road damage detection algorithms are unable to effectively distinguish and fuse multiple features. In this paper, we propose a dense multiscale feature learning Transformer embedding cross-shaped attention for road damage detection (DMTC) network, which can segment the damage information in road images and improve the effectiveness of road damage detection. Our DMTC makes three contributions. Firstly, we adopt a

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Awad, Mohamad M. "A Morphological Model for Extracting Road Networks from High-Resolution Satellite Images." Journal of Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/243021.

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Urban planning depends strongly on information extracted from high-resolution satellite images such as buildings and roads features. Nowadays, most of the available extraction techniques and methods are supervised, and they require intensive labor work to clean irrelevant features and to correct shapes and boundaries. In this paper, a new model is implemented to overcome the limitations and to correct the problems of the known and conventional techniques of urban feature extraction specifically road network. The major steps in the model are the enhancement of the image, the segmentation of the

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Li, P., Y. Li, J. Feng, Z. Ma, and X. Li. "AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 113–17. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-113-2020.

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Abstract. Automatic road extraction from remote sensing imagery is very useful for many applications involved with geographic information. For road extraction of urban areas, road intersections offer stable and reliable information for extraction of road network, with higher completeness and accuracy. In this paper, a segmentation-shape analysis based method is proposed to detect road intersections and their branch directions from an image. In the region of interest, it uses the contour shape of the segmented-intersection area to form a feature vector representing its geometric information. Th

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Shao, Shiwei, Lixia Xiao, Liupeng Lin, Chang Ren, and Jing Tian. "Road Extraction Convolutional Neural Network with Embedded Attention Mechanism for Remote Sensing Imagery." Remote Sensing 14, no. 9 (2022): 2061. http://dx.doi.org/10.3390/rs14092061.

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Roads are closely related to people’s lives, and road network extraction has become one of the most important remote sensing tasks. This study aimed to propose a road extraction network with an embedded attention mechanism to solve the problem of automatic extraction of road networks from a large number of remote sensing images. Channel attention mechanism and spatial attention mechanism were introduced to enhance the use of spectral information and spatial information based on the U-Net framework. Moreover, residual densely connected blocks were introduced to enhance feature reuse and informa

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Shao, Shiwei, Lixia Xiao, Liupeng Lin, Chang Ren, and Jing Tian. "Road Extraction Convolutional Neural Network with Embedded Attention Mechanism for Remote Sensing Imagery." Remote Sensing 14, no. 9 (2022): 2061. http://dx.doi.org/10.3390/rs14092061.

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Roads are closely related to people’s lives, and road network extraction has become one of the most important remote sensing tasks. This study aimed to propose a road extraction network with an embedded attention mechanism to solve the problem of automatic extraction of road networks from a large number of remote sensing images. Channel attention mechanism and spatial attention mechanism were introduced to enhance the use of spectral information and spatial information based on the U-Net framework. Moreover, residual densely connected blocks were introduced to enhance feature reuse and informa

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Medhi, Ankita, and Ashis Kumar Saha. "Rural Road Extraction using Object Based Image Analysis (OBIA): A case study from Assam, India." Advances in Cartography and GIScience of the ICA 1 (July 3, 2019): 1–8. http://dx.doi.org/10.5194/ica-adv-1-13-2019.

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<p><strong>Abstract.</strong> Rural roads in India have been considered as significant component for overall rural development. In India, the status of rural road connectivity is not up to the mark in some of the states. For providing better connectivity in the rural areas the information on roads are considered important. Detailed mapping of the roads can be useful for planning further road connectivity and proving access to facilities in the rural areas. For detailed mapping of roads higher resolution satellite imageries are required. Object based Image Analysis (OBIA) has

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Kumar, M., R. K. Singh, P. L. N. Raju, and Y. V. N. Krishnamurthy. "Road Network Extraction from High Resolution Multispectral Satellite Imagery Based on Object Oriented Techniques." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 107–10. http://dx.doi.org/10.5194/isprsannals-ii-8-107-2014.

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High Resolution satellite Imagery is an important source for road network extraction for urban road database creation, refinement and updating. However due to complexity of the scene in an urban environment, automated extraction of such features using various line and edge detection algorithms is limited. In this paper we present an integrated approach to extract road network from high resolution space imagery. The proposed approach begins with segmentation of the scene with Multi-resolution Object Oriented segmentation. This step focuses on exploiting both spatial and spectral information for

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Yang, Wenbing, Xiaoqi Gao, Chunlei Zhang, Feng Tong, Guantian Chen, and Zhijian Xiao. "Bridge Extraction Algorithm Based on Deep Learning and High-Resolution Satellite Image." Scientific Programming 2021 (June 2, 2021): 1–8. http://dx.doi.org/10.1155/2021/9961963.

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This paper proposes a novel method of extracting roads and bridges from high-resolution remote sensing images based on deep learning. Edge detection is performed on the images in the road area along with the road skeleton line, and the result of the detected binary edge is vectorized. The interference of protective belts on both sides of the road, road vehicles, road green belts, traffic signs, etc. and the shadow interference of the bridge itself are eliminated to determine the parallel sides of the road. The bridge features on the road are used to locate the detected bridge and obtain inform

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Krishna, R. Ramya, and N. Jyothi. "Road Surface Condition Identification with Deep Neural Networks and SVM Classifier." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 21998–2003. https://doi.org/10.48084/etasr.10166.

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Roads are people's main transportation mode, deeming them an important aspect of worldwide everyday life. However, weather conditions increasingly impact road infrastructure, necessitating improved road safety measures. Identifying road types enhances traffic management and safety, particularly as roads often sustain damage during the rainy season and require restoration that takes time. In many countries, weather conditions also affect road usability. This study proposes a Deep Neural Network (DNN) for automatic road classification Road Surface Images (RSI). ResNet-50 is employed for feature

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Lin, Shaofu, Xin Yao, Xiliang Liu, et al. "MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds." Remote Sensing 15, no. 13 (2023): 3367. http://dx.doi.org/10.3390/rs15133367.

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Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the robustness of road extraction methods. This study proposes a multi-scale road extraction method with asymmetric generative adversarial learning (MS-AGAN). First, we design an asymmetric GAN with a multi-scale feature encoder to better utilize the context information in high-resolution remote

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