Relevant bibliographies by topics / Road feature extraction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Road feature extraction'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Road feature extraction"

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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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Dissertations / Theses on the topic "Road feature extraction"

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Ozkaya, Meral. "Road Extraction From High-resolution Satellite Images." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610655/index.pdf.

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Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for different applications such as automated map generation and change detection. Roads are also important to detect other structures such as buildings and urban areas. In this thesis, the road extraction approach is based on Active Contour Models for 1- meter resolution gray level images. Active Contour Models contains Snake Approach. During applications, the road structure was separated as salient-roads, non-salient roads and crossings and extraction of these is provi
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Oller, Adam. "Automatic Mapping of Off-road Trails and Paths at Fort Riley Installation, Kansas." OpenSIUC, 2012. https://opensiuc.lib.siu.edu/theses/820.

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The U.S. Army manages thousands of sites that cover millions of acres of land for various military training purposes and activities and often faces a great challenge on how to optimize the use of resources. A typical example is that the training activities often lead to off-road vehicle trails and paths and how to use the trails and paths in terms of minimizing maintenance cost becomes a problem. Being able to accurately extract and map the trails and paths is critical in advancing the U.S. Army's sustainability practices. The primary objective of this study is to develop a method geared speci
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Sivaraman, Vijayaraghavan. "Rural road feature extraction from aerial images using anisotropic diffusion and dynamic snakes." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0007100.

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Gallis, Rodrigo Bezerra de Araújo [UNESP]. "Extração semi-automática da malha viária em imagens aéreas digitais de áreas rurais utilizando otimização por programação dinâmica no espaço objeto." Universidade Estadual Paulista (UNESP), 2006. http://hdl.handle.net/11449/100262.

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Made available in DSpace on 2014-06-11T19:30:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-10-31Bitstream added on 2014-06-13T20:47:04Z : No. of bitstreams: 1 gallis_rba_dr_prud.pdf: 3261376 bytes, checksum: 6967d0b5771ef57a837696cfb04efa2f (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)<br>Este trabalho propõe uma nova metodologia para extração de rodovias utilizando imagens aéreas digitais. A inovação baseia-se no algoritmo de Programação dinâmica (PD), que nesta metodologia realiza o
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Gallis, Rodrigo Bezerra de Araújo. "Extração semi-automática da malha viária em imagens aéreas digitais de áreas rurais utilizando otimização por programação dinâmica no espaço objeto /." Presidente Prudente : [s.n.], 2006. http://hdl.handle.net/11449/100262.

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Resumo: Este trabalho propõe uma nova metodologia para extração de rodovias utilizando imagens aéreas digitais. A inovação baseia-se no algoritmo de Programação dinâmica (PD), que nesta metodologia realiza o processo de otimização no espaço objeto, e não no espaço imagem como as metodologias tradicionais de extração de rodovias por PD. A feição rodovia é extraída no espaço objeto, o qual implica um rigoroso modelo matemático, que é necessário para estabelecer os pontos entre o espaço imagem e objeto. Necessita-se que o operador forneça alguns pontos sementes no espaço imagem para descrever gro
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Zelek, John S. "Automatic extraction of road features from remotely sensed imagery." Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5649.

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Repaka, Sunil Reddy. "Comparing spectral-object based approaches for extracting and classifying transportation features using high resolution multi-spectral satellite imagery." Master's thesis, Mississippi State : Mississippi State University, 2004. http://library.msstate.edu/etd/show.asp?etd=etd-11082004-231712.

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Zurita, Millán Daniel. "Contributions to industrial process condition forecasting applied to copper rod manufacturing process." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461087.

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Ensuring reliability and robustness of operation is one of the main concerns in industrial anufacturing processes , dueto the ever-increasing demand for improvements over the cost and quality ofthe processes outcome. In this regard , a deviation from the nominal operating behaviours implies a divergence from the optimal condition specification, anda misalignment from the nominal product quality, causing a critica! loss of potential earnings . lndeed, since a decade ago, the industrial sector has been carried out a significant effort<br>Asegurar la fiabilidad y la robustez es uno de los princip
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Heva, Prasadi Thilanka Senadeera Kanda Uda. "Automation of road feature extraction from high resolution images." Master's thesis, 2021. http://hdl.handle.net/10362/113905.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies<br>The detection of road features from remotely sensed images has become a critical factor in maintaining a reliable and updated road network in a country to provide a base reference for transportation, emergency planning, and navigation. With the recent advances of convolutional neural networks in image processing, several publications are devoted to the development of a method for automatically extract roads from satellite images. However, a reliable feature extract
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Hauptfleisch, Andries Carl. "Automatic road network extraction from high resolution satellite imagery using spectral classification methods." Diss., 2010. http://hdl.handle.net/2263/26866.

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Road networks play an important role in a number of geospatial applications, such as cartographic, infrastructure planning and traffic routing software. Automatic and semi-automatic road network extraction techniques have significantly increased the extraction rate of road networks. Automated processes still yield some erroneous and incomplete results and costly human intervention is still required to evaluate results and correct errors. With the aim of improving the accuracy of road extraction systems, three objectives are defined in this thesis: Firstly, the study seeks to develop a flexible
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Book chapters on the topic "Road feature extraction"

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Dong, Chenglin, Bin Feng, and Guoqiang Jia. "Data feature extraction method of intelligent vehicle road system test scene based on autoencoder." In Frontier Research: Road and Traffic Engineering. CRC Press, 2022. http://dx.doi.org/10.1201/9781003305002-116.

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Stilla, Uwe, and Karin Hedman. "Feature Fusion Based on Bayesian Network Theory for Automatic Road Extraction." In Radar Remote Sensing of Urban Areas. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3751-0_3.

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Gothane, Suwarna, M. V. Sarode, and V. M. Thakre. "Prediction for Indian Road Network Images Dataset Using Feature Extraction Method." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1580-0_12.

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Zeybek, Mustafa, and Serkan Biçici. "Geometric Feature Extraction of Road from UAV Based Point Cloud Data." In Innovations in Smart Cities Applications Volume 4. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66840-2_33.

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Maurya, Anamika, and Satish Chand. "Eff-MANet: A Systematic Approach for Feature Extraction in Unstructured Road Scene Scenarios." In Computational Intelligence in Analytics and Information Systems. Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003332367-28.

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Dong, Sijun, Yu Zhao, and Zhengchao Chen. "Remote Sensing Road Segmentation Based on Feature Extraction Optimization and Skeleton Detection Optimization." In Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020). Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5735-1_28.

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Liu, Peng, Yurong Qian, Hongyang Wei, Yugang Qin, and Yingying Fan. "BiReNet: Bilateral Network with Feature Aggregation and Edge Detection for Remote Sensing Images Road Extraction." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8493-6_28.

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Li, Xiang, Wenbing Liu, Xin Liu, and Jingyang Li. "Feature Extraction and Marking Method of Inertial Navigation Trajectory Based on Permutation Entropy Under Road Constraints." In Spatial Data and Intelligence. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24521-3_4.

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Biçici, Serkan, and Mustafa Zeybek. "Improvements on Road Centerline Extraction by Combining Voronoi Diagram and Intensity Feature from 3D UAV-Based Point Cloud." In Innovations in Smart Cities Applications Volume 5. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94191-8_76.

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Li, Xiang, Wenbing Liu, and Qun Chen. "Intelligent Extraction Method of Inertial Navigation Trajectory Behavior Features Considering Road Environment." In Spatial Data and Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85462-1_4.

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Conference papers on the topic "Road feature extraction"

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Pruthi, Jyoti, and Sunita Dhingra. "Comparative Analysis of Deep Learning Based Algorithms for Road Feature Extraction." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986538.

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Yamamoto, Jin, Ryuichi Imai, Kenji Nakamura, Yoshinori Tsukada, and Yoshimasa Umehara. "Consideration of Road Feature Extraction Using Low-Cost LiDAR Mounted in a Vehicle." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00310.

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Rajesh, N., Laith H. Alzubaidi, S. G. Shivaprasad Yadav, W. T. Chembian, and Boddepalli Prameela. "Automatic Road Extraction using Scale Invariant Feature Transform-based Random Forest in Satellite Images." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796473.

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Shekhar, Shivanshu, Lucia Gauchia, and Hortensia Amaris. "Feature Identification and Extraction for Battery Aging Estimation in Aircraft Auxiliary Applications." In 2024 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC). IEEE, 2024. https://doi.org/10.1109/esars-itec60450.2024.10819891.

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Li, Ming, Zhengzhang Yang, Qiang Zhao, and Yu Liu. "A Road Extraction Method Based on Dual-Domain Feature Fusion and Multi-Stage Fine-Tuned SAM." In 2025 6th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM). IEEE, 2025. https://doi.org/10.1109/icmtim65484.2025.11040723.

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Ke, Yansheng, and Jing Wang. "A Remote Sensing Road Extraction Model Based on Improved Deeplabv3+ with Adaptive Multi-Scale Feature Fusion Pyramid." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762525.

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Kim, Hyunjun, Kwangjun Jung, Sumin Lee, Jongyoon Han, Jaeyoung Song, and Mi-Seon Kang. "Machine Learning Model for Detecting Leakage in Water Distribution Networks Through Road Surface Leakage Noise Analysis: Feature Extraction Using Fourier Transform and MFCC." In 2024 International Conference on Platform Technology and Service (PlatCon). IEEE, 2024. https://doi.org/10.1109/platcon63925.2024.10830668.

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Veit, Thomas, Jean-Philippe Tarel, Philippe Nicolle, and Pierre Charbonnier. "Evaluation of Road Marking Feature Extraction." In 2008 11th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2008. http://dx.doi.org/10.1109/itsc.2008.4732564.

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Negri, M., and P. Gamba. "Feature Fusion for Road Extraction in SAR Scenes." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.650.

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Gopan, Archa, and Abid Hussain Muhammed. "Dehazing and Road Feature Extraction from Satellite Images." In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT). IEEE, 2019. http://dx.doi.org/10.1109/iciict1.2019.8741492.

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Reports on the topic "Road feature extraction"

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Griffin, Andrew, Sean Griffin, Kristofer Lasko, et al. Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40182.

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Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting
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Blundell, S. Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40185.

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Elevation models derived from high-resolution airborne lidar scanners provide an added dimension for identification and extraction of micro-terrain features characterized by topographic discontinuities or breaklines. Gridded digital surface models created from first-return lidar pulses are often combined with lidar-derived bare-earth models to extract vegetation features by model differencing. However, vegetative canopy can also be extracted from the digital surface model alone through breakline analysis by taking advantage of the fine-scale changes in slope that are detectable in high-resolut
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Chien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu, and Wei Lin. Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT's Traffic Management System. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317400.

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The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and
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