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Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

Abolfazl Abdollahi, Biswajeet Pradhan, Nagesh Shukla, Subrata Chakraborty, Abdullah Alamri
2020 Remote Sensing  
We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images.  ...  One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images.  ...  [49] presented an approach for road centerline extraction from high-resolution remote sensing imagery that comprised four major stages.  ... 
doi:10.3390/rs12091444 fatcat:bjxn3uh2wrc3joiejtj4ksrqx4


Y. Wei, X. Hu, M. Zhang, Y. Xu
2020 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Extracting roads from aerial images is a challenging task in the field of remote sensing.  ...  In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps.  ...  INTRODUCTION Road extraction from high-resolution remote sensing images is an essential task in the field of remote sensing.  ... 
doi:10.5194/isprs-annals-v-2-2020-925-2020 fatcat:gocr6s5jfjhyrlkwdeo3ljr4jm


Doaa M.-A. Latif, Mohammed A.-M. Salem, Mohamed Roushdy
2022 Journal of Southwest Jiaotong University  
The remotely sensed images were analyzed and inspected for automatic road network generation through the past years.  ...  This paper presents a comprehensive overview along with an experimental comparison of four widely used deep learning architectures applied to the automatic road network generation problem.  ...  centerline extraction from VHR imagery via multiscale segmentation and tensor voting.  ... 
doi:10.35741/issn.0258-2724.57.1.28 fatcat:5ychrnciwjgwpck6wxfkiqgkg4

Forest Roads Mapped Using LiDAR in Steep Forested Terrain

Russell A. White, Brian C. Dietterick, Thomas Mastin, Rollin Strohman
2010 Remote Sensing  
LiDAR can be particularly valuable in forested areas by providing accurate measurements of ground surface elevations, permitting high-resolution topographic mapping, even under dense vegetation [20] .  ...  The position, gradient, and total length of a forest haul road were accurately extracted using a 1 m DEM.  ...  Acknowledgements Partial funding for this research was provided through the CSU Agricultural Research Initiative with support from Cal Poly Swanton Pacific Ranch.  ... 
doi:10.3390/rs2041120 fatcat:jrcefdkcmfguvhe2otwwl3xawy

Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images

Lipeng Gao, Wenzhong Shi, Zelang Miao, Zhiyong Lv
2018 Remote Sensing  
In recent decades, road extraction from very high-resolution (VHR) remote sensing images has become popular and has attracted extensive research efforts.  ...  Thus, this study presents a semiautomatic edge-constraint fast marching (ECFM) method to extract road centerlines from VHR images.  ...  [31] proposed a multifeature sparsity-based model that can utilize multifeature complementation to extract roads from high-resolution imagery. Dal Poz et al.  ... 
doi:10.3390/rs10060900 fatcat:7y3jkutfmbfotpptgy6qfbhhr4

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Renbao Lian, Weixing Wang, Nadir Mustafa, Liqin Huang
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Road extraction from high-resolution remote sensing images (HRSI) is a challenging but hot research topic in the past decades. A large number of methods are invented to deal with this problem.  ...  With regard to the heuristic methods, the road feature model is firstly introduced, then, the classic extraction methods are reviewed in two sub-categories: semi-automatic and automatic.  ...  This work was supported by grants from the Natural Science Foundation of China (No. 61170147), Education and Scientific Research Project for Middle-aged and Young Teachers in Fujian Province (No.  ... 
doi:10.1109/jstars.2020.3023549 fatcat:cpxewtt2xrf7zf74gyppeuzsv4

Foreword to the Special Issue on Pattern Recognition in Remote Sensing

Qian Du, Eckart Michaelsen, Peijun Du, Lorenzo Bruzzone, Xiaohua Tong, Uwe Stilla
2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In [18] , an accurate road centerline extraction method from HSR multispectral images is presented, which integrates tensor voting, principal curves, and the geodesic method to cope with complicated road  ...  In [6] , the bag-of-visual words (BOVWs) model is applied to high spatial-resolution (HSR) image classification and categorization; specifically, a concentric circle-structured multiscale BOVW method  ... 
doi:10.1109/jstars.2014.2384931 fatcat:wrxey326lvfjbb4clilwdpa7rm

Multiscale Centerline Detection

Amos Sironi, Engin Turetken, Vincent Lepetit, Pascal Fua
2016 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Finding the centerline and estimating the radius of linear structures is a critical first step in many applications, ranging from road delineation in 2D aerial images to modeling blood vessels, lung bronchi  ...  The former tend to become unreliable when the linear structures are very irregular while the latter often has difficulties distinguishing centerline locations from neighboring ones, thus losing accuracy  ...  We tested our method on the 2D road images and 3D biological image stacks depicted by Fig. 7 . More specifically we used the following datasets: • Aerial: Aerial images of road networks.  ... 
doi:10.1109/tpami.2015.2462363 pmid:27295457 fatcat:ltv2unsh2fetda27lvb7lqosei

Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery

Dejun Feng, Xingyu Shen, Yakun Xie, Yangge Liu, Jian Wang
2021 Remote Sensing  
Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.  ...  Road extraction is important for road network renewal, intelligent transportation systems and smart cities.  ...  Efficient Occluded Road Extraction from High- Resolution Remote Sensing Imagery. Remote Sens. 2021, 13, 4974. https:// 1.  ... 
doi:10.3390/rs13244974 fatcat:rr6zwwswgrbvhlamwbhrjleehm

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Centerline Extraction From High-Resolution Remote Sensing Images.  ...  ., +, JSTARS 2020 3958-3974 A Self-Supervised Learning Framework for Road Centerline Extraction From High-Resolution Remote Sensing Images.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

DeepWindow: Sliding Window Based on Deep Learning for Road Extraction from Remote Sensing Images

Renbao Lian, Liqin Huang
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The road centerline extraction is the key step of the road network extraction and modeling.  ...  The hand-craft feature engineering in the traditional road extraction methods is unstable, which makes the extracted road centerline deviated from the road center in complex cases and even results in overall  ...  High-resolution remote sensing imagery is an important avenue to automatically infer the road networks.  ... 
doi:10.1109/jstars.2020.2983788 fatcat:xz7miigswzbhzn67ctfkz53dtm


W. Zhang, B. Hu, L. Quist
2017 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The algorithm utilized Laplacian of Gaussian (LoG) filter and slope calculation on high resolution multispectral imagery and LiDAR data respectively to extract both primary road and secondary road segments  ...  Based on visual examination against manually digitized roads, the majority of roads from the test area have been identified and extracted from the process.  ...  resolution aerial imagery.  ... 
doi:10.5194/isprs-archives-xlii-3-w3-201-2017 fatcat:azlw4aoq6fco7isyari3nhmdam

Automatic Road Extraction based on Normalized Cuts and Level set Methods

M. Rajeswari, K.S. Gurumurthy, L.Pratap Reddy, S.N. Omkar, Senthilnath. J
2011 International Journal of Computer Applications  
INTRODUCTION Recent methods for extraction of roads from high resolution Road extraction from remotely sensed images has always imagery  ...  Pattern Road Junction Islands from High Resolution Aerial Recogn. Lett. 26(9), 2005pp.1201-1220.  ... 
doi:10.5120/2298-2988 fatcat:shc3smbgardjzhjbuzui7vy3ky

Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss

Hao He, Dongfang Yang, Shicheng Wang, Shuyang Wang, Yongfei Li
2019 Remote Sensing  
The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications.  ...  The proposed approach takes advantage of ASPP's ability to extract multiscale features and the Encoder-Decoder network's ability to extract detailed features.  ...  In the problem of road extraction from remote sensing images, it is necessary to ensure a high-detail resolution of segmentation maps.  ... 
doi:10.3390/rs11091015 fatcat:txlp2taro5bzpaocox4jeghjty

Multi-modal Urban Remote Sensing Image Registration via Roadcross Triangular Feature

Kun Yu, Xiao Zheng, Bin Fang, Pei An, Xiao Huang, Wei Luo, Junfeng Ding, Zhao Wang, Jie Ma
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The proposed method obtains three main stages: road lines extraction from images, intersection triangular feature construction, and triangular feature matching.  ...  Index Terms-Multi-modal image registration, road intersection triangular feature, urban remote sensing, image matching.  ...  We utilize SpaceNet3 satellite imagery and geocoded road centerline labels to build training datasets for our models.  ... 
doi:10.1109/jstars.2021.3073573 fatcat:7uuhao77fzhtplkxvygu6lfzj4
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