Tournaire, O.[Olivier],
Paparoditis, N.[Nicolas],
A geometric stochastic approach based on marked point processes for
road mark detection from high resolution aerial images,
PandRS(64), No. 6, November 2009, pp. 621-631.
Elsevier DOI
1001
Road modelling; Aerial imagery; Road marks; Marked point process; RJMCMC
BibRef
Tournaire, O.[Olivier],
Paparoditis, N.[Nicolas],
Lafarge, F.,
Rectangular Road Marking Detection with Marked Point Processes,
PIA07(149).
PDF File.
0711
BibRef
Soheilian, B.[Bahman],
Paparoditis, N.[Nicolas],
Boldo, D.[Didier],
3D road marking reconstruction from street-level calibrated stereo
pairs,
PandRS(65), No. 4, July 2010, pp. 347-359.
Elsevier DOI
1003
Mobile mapping system; Edge matching;
Shape from stereo; 3D road marking extraction
BibRef
Tournaire, O.[Olivier],
Paparoditis, N.[Nicolas],
Jung, F.[Franck],
Cervelle, B.[Bernard],
3D Road-Mark Reconstruction from Multiple Calibrated Aerial Images,
PCV06(xx-yy).
PDF File.
0609
BibRef
Kheyrollahi, A.[Alireza],
Breckon, T.P.[Toby P.],
Automatic real-time road marking recognition using a feature driven
approach,
MVA(23), No. 1, January 2012, pp. 123-133.
WWW Link.
1201
BibRef
Guan, H.Y.[Hai-Yan],
Li, J.,
Yu, Y.T.[Yong-Tao],
Ji, Z.[Zheng],
Wang, C.[Cheng],
Using Mobile LiDAR Data for Rapidly Updating Road Markings,
ITS(16), No. 5, October 2015, pp. 2457-2466.
IEEE DOI
1511
feature extraction
BibRef
Soilán, M.[Mario],
Riveiro, B.[Belén],
Martínez-Sánchez, J.[Joaquín],
Arias, P.[Pedro],
Segmentation and classification of road markings using MLS data,
PandRS(123), No. 1, 2017, pp. 94-103.
Elsevier DOI
1612
Mobile laser scanning
BibRef
Guan, H.Y.[Hai-Yan],
Li, J.[Jonathan],
Yu, Y.T.[Yong-Tao],
Wang, C.[Cheng],
Chapman, M.[Michael],
Yang, B.S.[Bi-Sheng],
Using Mobile Laser Scanning Data for Automated Extraction of Road
Markings,
PandRS(87), No. 1, 2014, pp. 93-107.
Elsevier DOI
1402
MLS data
See also Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data.
BibRef
Guan, H.Y.[Hai-Yan],
Li, J.[Jonathan],
Yu, Y.T.[Yong-Tao],
Chapman, M.[Michael],
Wang, C.[Cheng],
Automated Road Information Extraction from Mobile Laser Scanning Data,
ITS(16), No. 1, February 2015, pp. 194-205.
IEEE DOI
1502
See also Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data.
See also Automated Detection of Three-Dimensional Cars in Mobile Laser Scanning Point Clouds Using DBM-Hough-Forests. Accuracy
BibRef
Zai, D.W.[Da-Wei],
Li, J.[Jonathan],
Guo, Y.L.[Yu-Lan],
Cheng, M.[Ming],
Lin, Y.B.[Yang-Bin],
Luo, H.[Huan],
Wang, C.[Cheng],
3-D Road Boundary Extraction From Mobile Laser Scanning Data via
Supervoxels and Graph Cuts,
ITS(19), No. 3, March 2018, pp. 802-813.
IEEE DOI
1804
Clustering algorithms, Data mining, Feature extraction, Lasers,
Mobile communication, Roads,
supervoxel
BibRef
Yu, Y.T.[Yong-Tao],
Guan, H.,
Ji, Z.,
Automated Detection of Urban Road Manhole Covers Using Mobile Laser
Scanning Data,
ITS(16), No. 6, December 2015, pp. 3258-3269.
IEEE DOI
1512
Algorithm design and analysis
BibRef
Pan, Y.[Yue],
Yang, B.S.[Bi-Sheng],
Li, S.F.[Sheng-Fu],
Yang, H.[Hong],
Dong, Z.[Zhen],
Yang, X.[Xue],
Automatic Road Markings Extraction, Classification and Vectorization
From Mobile Laser Scanning Data,
Laser19(1089-1096).
DOI Link
1912
BibRef
Fischer, P.[Peter],
Azimi, S.M.[Seyed Majid],
Roschlaub, R.[Robert],
Krauß, T.[Thomas],
Towards HD Maps from Aerial Imagery:
Robust Lane Marking Segmentation Using Country-Scale Imagery,
IJGI(7), No. 12, 2018, pp. xx-yy.
DOI Link
1901
BibRef
Azimi, S.M.,
Fischer, P.,
Körner, M.,
Reinartz, P.,
Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery
Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional
Neural Networks,
GeoRS(57), No. 5, May 2019, pp. 2920-2938.
IEEE DOI
1905
convolutional neural nets,
driver information systems, feature extraction,
wavelet transform
BibRef
Chen, S.Y.[Si-Yun],
Zhang, Z.X.[Zhen-Xin],
Zhong, R.F.[Ruo-Fei],
Zhang, L.Q.[Li-Qiang],
Ma, H.[Hao],
Liu, L.R.[Li-Rong],
A Dense Feature Pyramid Network-Based Deep Learning Model for Road
Marking Instance Segmentation Using MLS Point Clouds,
GeoRS(59), No. 1, January 2021, pp. 784-800.
IEEE DOI
2012
Roads, Feature extraction,
Deep learning, Image segmentation, Data mining, Remote sensing,
road markings
BibRef
Mi, X.X.[Xiao-Xin],
Yang, B.S.[Bi-Sheng],
Dong, Z.[Zhen],
Liu, C.[Chong],
Zong, Z.L.[Ze-Liang],
Yuan, Z.C.[Zhen-Chao],
A two-stage approach for road marking extraction and modeling using
MLS point clouds,
PandRS(180), 2021, pp. 255-268.
Elsevier DOI
2109
Mobile laser scanning (MLS) point clouds, Road markings,
Extraction, Modeling, Object detection, Shape matching
BibRef
Ma, L.F.[Ling-Fei],
Li, Y.[Ying],
Li, J.[Jonathan],
Yu, Y.T.[Yong-Tao],
Marcato Junior, J.[José],
Gonçalves, W.N.[Wesley Nunes],
Chapman, M.A.[Michael A.],
Capsule-Based Networks for Road Marking Extraction and Classification
from Mobile LiDAR Point Clouds,
ITS(22), No. 4, April 2021, pp. 1981-1995.
IEEE DOI
2104
Roads, Feature extraction, Laser radar,
Image segmentation,
dynamic routing
See also Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds.
See also Rapid Extraction of Urban Road Guardrails from Mobile LiDAR Point Clouds.
BibRef
Luo, H.[Huan],
Wang, C.[Cheng],
Wen, C.L.[Cheng-Lu],
Cai, Z.P.[Zhi-Peng],
Chen, Z.Y.[Zi-Yi],
Wang, H.Y.[Han-Yun],
Yu, Y.T.[Yong-Tao],
Li, J.[Jonathan],
Patch-Based Semantic Labeling of Road Scene Using Colorized Mobile
LiDAR Point Clouds,
ITS(17), No. 5, May 2016, pp. 1286-1297.
IEEE DOI
1605
Context
BibRef
Rastiveis, H.[Heidar],
Shams, A.[Alireza],
Sarasua, W.A.[Wayne A.],
Li, J.[Jonathan],
Automated Extraction of Lane Markings from Mobile LiDAR Point Clouds
Based on Fuzzy Inference,
PandRS(160), 2020, pp. 149-166.
Elsevier DOI
2001
Mobile LiDAR, Road lane markings, Point cloud, Fuzzy inference system
BibRef
Zhang, H.C.[Hao-Cheng],
Li, J.[Jonathan],
Cheng, M.[Ming],
Wang, C.[Cheng],
Rapid Inspection of Pavement Markings Using Mobile LiDAR Point Clouds,
ISPRS16(B1: 717-723).
DOI Link
1610
BibRef
Kong, W.[Wanyue],
Zhong, T.[Teng],
Mai, X.[Xin],
Zhang, S.[Shuliang],
Chen, M.[Min],
Lv, G.N.[Guo-Nian],
Automatic Detection and Assessment of Pavement Marking Defects with
Street View Imagery at the City Scale,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Wu, J.J.[Jun-Jie],
Liu, W.[Wen],
Maruyama, Y.[Yoshihisa],
Automated Road-Marking Segmentation via a Multiscale Attention-Based
Dilated Convolutional Neural Network Using the Road Marking Dataset,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Cheng, Y.T.[Yi-Ting],
Lin, Y.C.[Yi-Chun],
Habib, A.[Ayman],
Generalized LiDAR Intensity Normalization and Its Positive Impact on
Geometric and Learning-Based Lane Marking Detection,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Li, C.G.[Chen-Guang],
Shi, J.[Jia],
Wang, Y.[Ya],
Cheng, G.L.[Guang-Liang],
Reconstruct from Top View: A 3D Lane Detection Approach based on
Geometry Structure Prior,
WAD22(4369-4378)
IEEE DOI
2210
Geometry, Image segmentation, Lane detection, Pipelines,
Training data, Feature extraction
BibRef
Behrendt, K.,
Soussan, R.,
Unsupervised Labeled Lane Markers Using Maps,
CVRSUAD19(832-839)
IEEE DOI
2004
Code, Lane Detection.
WWW Link. image segmentation, optimisation, regression analysis,
unsupervised learning, high-quality lane marker datasets, dataset
BibRef
Hazelhoff, L.[Lykele],
Creusen, I.[Ivo],
Woudsma, T.[Thomas],
Bao, X.F.[Xin-Feng],
de With, P.H.N.[Peter H.N.],
Combined generation of road marking and road sign databases applied
to consistency checking of pedestrian crossings,
MVA15(439-442)
IEEE DOI
1507
Databases
BibRef
Ishida, H.[Hiroyuki],
Kidono, K.[Kiyosumi],
Kojima, Y.[Yoshiko],
Naito, T.[Takashi],
Road marking recognition for map generation using sparse tensor voting,
ICPR12(1132-1135).
WWW Link.
1302
BibRef
Suzuki, T.[Tomohisa],
Kodaira, N.[Naoaki],
Mizutani, H.[Hiroyuki],
Nakai, H.[Hiroaki],
Shinohara, Y.[Yasuo],
A Binarization Algorithm Based on Shade-Planes for Road Marking
Recognition,
SCIA09(51-60).
Springer DOI
0906
BibRef
Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Power Line Extraction, Powerline Extraction, Radar, SAR, Lidar, Laser, Depth .