Utility Mapping, Buried Utilities, Pipelines, Subsurface Infrastructure

Chapter Contents (Back)
Buried Objects. Ground Penetrating. Utilities. Radar.

Osella, A., Martinelli, P., Favetto, A.B., Lopez, E.,
Induction effects of 2-D structures on buried pipelines,
GeoRS(40), No. 1, January 2002, pp. 197-205.
IEEE Top Reference. 0203

Borgioli, G., Capineri, L., Falorni, P., Matucci, S., Windsor, C.G.,
The Detection of Buried Pipes From Time-of-Flight Radar Data,
GeoRS(46), No. 8, August 2008, pp. 2254-2266.

Pettinelli, E., di Matteo, A., Mattei, E., Crocco, L., Soldovieri, F., Redman, J.D., Annan, A.P.,
GPR Response From Buried Pipes: Measurement on Field Site and Tomographic Reconstructions,
GeoRS(47), No. 8, August 2009, pp. 2639-2645.

Khan, U.S.[Umar S.], Al-Nuaimy, W.[Waleed], El-Samie, F.E.A.[Fathi E. Abd],
Detection of landmines and underground utilities from acoustic and GPR images with a cepstral approach,
JVCIR(21), No. 7, October 2010, pp. 731-740.
Elsevier DOI 1003
Landmine detection; GPR; Acoustic images; MFCCs; Polynomial coefficients; Discrete cosine transform (DCT); Discrete sine transform (DST); Discrete wavelet transform (DWT) BibRef

Boniger, U., Tronicke, J.,
Subsurface Utility Extraction and Characterization: Combining GPR Symmetry and Polarization Attributes,
GeoRS(50), No. 3, March 2012, pp. 736-746.

Camilo, J.A., Collins, L.M., Malof, J.M.,
A Large Comparison of Feature-Based Approaches for Buried Target Classification in Forward-Looking Ground-Penetrating Radar,
GeoRS(56), No. 1, January 2018, pp. 547-558.
Antenna arrays, Array signal processing, Feature extraction, Ground penetrating radar, Pipelines, Radar imaging, radar imaging BibRef

Zhou, X., Chen, H., Li, J.,
An Automatic GPR B-Scan Image Interpreting Model,
GeoRS(56), No. 6, June 2018, pp. 3398-3412.
Clustering algorithms, Estimation, Feature extraction, Ground penetrating radar, Noise measurement, Pipelines, Transforms, image processing BibRef

Wang, S.[Shuai], Guo, Q.S.[Qing-Sheng], Xu, X.L.[Xing-Lin], Xie, Y.[Yuwu],
A Study on a Matching Algorithm for Urban Underground Pipelines,
IJGI(8), No. 8, 2019, pp. xx-yy.
DOI Link 1909

Bernatek-Jakiel, A.[Anita], Kondracka, M.[Marta],
Detection of Soil Pipes Using Ground Penetrating Radar,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909

Meng, L.X.[Ling-Xuan], Peng, Z.X.[Zhi-Xing], Zhou, J.[Ji], Zhang, J.[Jirong], Lu, Z.Y.[Zhen-Yu], Baumann, A.[Andreas], Du, Y.[Yan],
Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001

Šarlah, N.[Nikolaj], Podobnikar, T.[Tomaž], Ambrožic, T.[Tomaž], Mušic, B.[Branko],
Application of Kinematic GPR-TPS Model with High 3D Georeference Accuracy for Underground Utility Infrastructure Mapping: A Case Study from Urban Sites in Celje, Slovenia,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004

Radulovic, A.[Aleksandra], Sladic, D.[Dubravka], Govedarica, M.[Miro], Ristic, A.[Aleksandar], Jovanovic, D.[Dušan],
LADM Based Utility Network Cadastre in Serbia,
IJGI(8), No. 5, 2019, pp. xx-yy.
DOI Link 1906

Yan, J.Y.[Jing-Ya], Jaw, S.W.[Siow Wei], Soon, K.H.[Kean Huat], Wieser, A.[Andreas], Schrotter, G.[Gerhard],
Towards an Underground Utilities 3D Data Model for Land Administration,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909

Gabrys, M.[Marta], Ortyl, L.[Lukasz],
Georeferencing of Multi-Channel GPR: Accuracy and Efficiency of Mapping of Underground Utility Networks,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009

Jin, Y.[Yang], Duan, Y.L.[Yun-Ling],
Wavelet Scattering Network-Based Machine Learning for Ground Penetrating Radar Imaging: Application in Pipeline Identification,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Amoroso, N.[Nicola], Cilli, R.[Roberto], Bellantuono, L.[Loredana], Massimi, V.[Vincenzo], Monaco, A.[Alfonso], Nitti, D.O.[Davide Oscar], Nutricato, R.[Raffaele], Samarelli, S.[Sergio], Taggio, N.[Niccolò], Tangaro, S.[Sabina], Tateo, A.[Andrea], Guerriero, L.[Luciano], Bellotti, R.[Roberto],
PSI Clustering for the Assessment of Underground Infrastructure Deterioration,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Hu, Z.[Zihe], Guo, J.[Jing], Zhang, X.Q.[Xue-Quan],
Three-Dimensional (3D) Parametric Modeling and Organization for Web-Based Visualization of City-Scale Pipe Network,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012

Solla, M.[Mercedes], Pérez-Gracia, V.[Vega], Fontul, S.[Simona],
A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103

Yang, H.[Han], Xu, H.C.[Hong-Cheng], Jiao, S.J.[Shuang-Jian], Yin, F.D.[Feng-De],
Semantic Image Segmentation Based Cable Vibration Frequency Visual Monitoring Using Modified Convolutional Neural Network with Pixel-wise Weighting Strategy,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104

Shahri, A.A.[Abbas Abbaszadeh], Kheiri, A.[Ali], Hamzeh, A.[Aliakbar],
Subsurface Topographic Modeling Using Geospatial and Data Driven Algorithm,
IJGI(10), No. 5, 2021, pp. xx-yy.
DOI Link 2106

Yamaguchi, T.[Takahiro], Mizutani, T.[Tsukasa], Nagayama, T.[Tomonori],
Mapping Subsurface Utility Pipes by 3-D Convolutional Neural Network and Kirchhoff Migration Using GPR Images,
GeoRS(59), No. 8, August 2021, pp. 6525-6536.
Training, Detection algorithms, Antenna measurements, Radar imaging, Convolutional neural networks, Inspection, subsurface utility pipes BibRef

Iftimie, N.[Nicoleta], Savin, A.[Adriana], Steigmann, R.[Rozina], Dobrescu, G.S.[Gabriel Silviu],
Underground Pipeline Identification into a Non-Destructive Case Study Based on Ground-Penetrating Radar Imaging,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

Zheng, G.[Gen], Zhao, J.H.[Jian-Hu], Li, S.B.[Shao-Bo], Feng, J.[Jie],
Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112

Donini, E.[Elena], Carrer, L.[Leonardo], Gerekos, C.[Christopher], Bruzzone, L.[Lorenzo], Bovolo, F.[Francesca],
An Unsupervised Fuzzy System for the Automatic Detection of Candidate Lava Tubes in Radar Sounder Data,
GeoRS(60), 2022, pp. 1-19.
Electron tubes, Radar, Moon, Surface waves, Radar detection, Optical surface waves, Mars, Fuzzy logic, image processing, subsurface BibRef

Heggy, E.[Essam], Normand, J.[Jonathan], Palmer, E.M.[Elizabeth M.], Abotalib, A.Z.[Abotalib Z.],
Exploring the nature of buried linear features in the Qatar peninsula: Archaeological and paleoclimatic implications,
PandRS(183), 2022, pp. 210-227.
Elsevier DOI 2201
Optical and radar imagery, Desert hydrology, Structural elements, Land classification, Paleoclimate, Archaeology BibRef

Jaufer, R.M.[Rakeeb Mohamed], Ihamouten, A.[Amine], Goyat, Y.[Yann], Todkar, S.S.[Shreedhar Savant], Guilbert, D.[David], Assaf, A.[Ali], Dérobert, X.[Xavier],
A Preliminary Numerical Study to Compare the Physical Method and Machine Learning Methods Applied to GPR Data for Underground Utility Network Characterization,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202

Li, F.[Fanruo], Yang, F.[Feng], Yan, R.[Rui], Qiao, X.[Xu], Xing, H.J.[Hong-Jia], Li, Y.J.[Yi-Jin],
Study on Significance Enhancement Algorithm of Abnormal Features of Urban Road Ground Penetrating Radar Images,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205

Feng, D.S.[De-Shan], Ding, S.Y.[Si-Yuan], Wang, X.[Xun], Su, X.[Xuan], Liu, S.[Shuo], Cao, C.[Cen],
Wavefield Reconstruction Inversion Based on the Multi-Scale Cumulative Frequency Strategy for Ground-Penetrating Radar Data: Application to Urban Underground Pipeline,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Hartshorn, C.A.[Caylin A.], Isaacson, S.D.[Sven D.], Barrowes, B.E.[Benjamin E.], Perren, L.J.[Lee J.], Lozano, D.[David], Shubitidze, F.[Fridon],
Analysis of the Feasibility of UAS-Based EMI Sensing for Underground Utilities Detection and Mapping,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Popov, A.V., Reznikov, A.E., Berkut, A.I., Edemsky, D.E., Morozov, P.A., Prokopovich, I.V.,
Methods and Algorithms of Subsurface Holographic Sounding,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211

Zhu, C.K.[Cheng-Ke], Ye, H.X.[Hong-Xia],
A Modular Method for GPR Hyperbolic Feature Detection and Quantitative Parameter Inversion of Underground Pipelines,
RS(15), No. 8, 2023, pp. 2114.
DOI Link 2305

Zhang, D.H.[Dong-Hao], Wang, Z.Z.[Zheng-Zheng], Qin, H.[Hui], Geng, T.[Tiesuo], Pan, S.[Shengshan],
GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures,
RS(15), No. 14, 2023, pp. 3650.
DOI Link 2307

Gamal, M.[Mohamed], Di, Q.Y.[Qing-Yun], Zhang, J.H.[Jin-Hai], Fu, C.M.[Chang-Min], Ebrahim, S.[Shereen], El-Raouf, A.A.[Amr Abd],
Utilizing Ground-Penetrating Radar for Water Leak Detection and Pipe Material Characterization in Environmental Studies: A Case Study,
RS(15), No. 20, 2023, pp. 4924.
DOI Link 2310

Wei, L.X.[Ling-Xiang], Guo, D.[Dongjun], Chen, Z.L.[Zhi-Long], Hu, Y.Y.[Ying-Ying], Wu, Y.H.[Yan-Hua], Ji, J.[Junyuan],
Growth Simulations of Urban Underground Space with Ecological Constraints Using a Patch-Based Cellular Automaton,
IJGI(12), No. 10, 2023, pp. 387.
DOI Link 2311

Lin, Y.[Yun], Wang, J.[Jiachun], Ma, D.[Deyun], Wang, Y.P.[Yan-Ping], Ye, S.[Shengbo],
Improved Cycle-Consistency Generative Adversarial Network-Based Clutter Suppression Methods for Ground-Penetrating Radar Pipeline Data,
RS(16), No. 6, 2024, pp. 1043.
DOI Link 2403

Zhang, J.[Ju], Hu, Q.W.[Qing-Wu], Zhou, Y.[Yemei], Zhao, P.C.[Peng-Cheng], Duan, X.[Xuzhe],
A Multi-Level Robust Positioning Method for Three-Dimensional Ground Penetrating Radar (3D GPR) Road Underground Imaging in Dense Urban Areas,
RS(16), No. 9, 2024, pp. 1559.
DOI Link 2405

Liu, Y.[Yi], Zhang, X.[Xuan], Li, Y.[Ying], Liang, G.X.[Gui-Xin], Jiang, Y.B.[Ya-Bing], Qiu, L.X.[Li-Xia], Tang, H.P.[Hai-Ping], Xie, F.[Fei], Yao, W.[Wei], Dai, Y.[Yi], Qiao, Y.[Yu], Wang, Y.[Yali],
VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection,
Location awareness, Measurement, Machine learning algorithms, Smart cities, Face recognition, Benchmark testing BibRef

Zhao, H.T., Zhou, J., Jing, C.F., Li, X.F.,
A Praxis on Data Quality Evaluation of Underground Pipeline,
ISPRS21(B3-2021: 811-816).
DOI Link 2201

Chen, Z., Pouliot, J., Hubert, F.,
A First Attempt to Define Level of Details Based on Decision-making Tasks: Application to Underground Utility Network,
DOI Link 2201

Hansen, L.H., van Son, R., Wieser, A., Kjems, E.,
Addressing the Elephant In the Underground: An Argument for The Integration of Heterogeneous Data Sources for Reconciliation Of Subsurface Utility Data,
DOI Link 2201

Hansen, L.H., Pedersen, T.M., Kjems, E., Wyke, S.,
Smartphone-based Reality Capture for Subsurface Utilities: Experiences From Water Utility Companies In Denmark,
DOI Link 2201
Data integration and information fusion BibRef

Feng, J.L.[Jing-Lun], Yang, L.[Liang], Wang, H.Y.[Hai-Yan], Tian, Y.L.[Ying-Li], Xiao, J.Z.[Ji-Zhong],
Subsurface Pipes Detection Using DNN-based Back Projection on GPR Data,
Location awareness, Learning systems, MIMICs, Radar imaging, Feature extraction, Robustness BibRef

Lieberman, J., Roensdorf, C.,
Modular Approach to 3d Representation of Underground Infrastructure In The Model for Underground Data Definition and Integration (MUDDI),
DOI Link 2012

Caselli, A., Falquet, G., Métral, C.,
Knowledge Graph Construction for Subsurface Objects Including Uncertainty and Time Variation,
DOI Link 2201

Métral, C., Daponte, V., Caselli, A., di Marzo, G., Falquet, G.,
Ontology-based Rule Compliance Checking for Subsurface Objects,
DOI Link 2012

Yan, J., Jaw, S.W., Soon, K.H., Schrotter, G.,
The LADM-Based 3D Underground Utility Mapping: Case Study in Singapore,
DOI Link 1912

van Son, R., Jaw, S.W., Wieser, A.,
A Data Capture Framework for Improving The Quality of Subsurface Utility Information,
DOI Link 1912

Tabarro, P.G., Pouliot, J., Fortier, R., Losier, L.M.,
A Webgis to Support GPR 3D Data Acquisition: A First Step for The Integration of Underground Utility Networks in 3D City Models,
DOI Link 1805

Cazzaniga, N.E., Pagliari, D., Pinto, L.,
Photogrammetry For Mapping Underground Utility Lines With Ground Penetrating Radar In Urban Areas,
DOI Link 1209

van Son, R., Jaw, S.W., Yan, J., Khoo, V., Loo, R., Teo, S., Schrotter, G.,
A Framework for Reliable Three-dimensional Underground Utility Mapping For Urban Planning,
DOI Link 1901

Pouliot, J., Larrivée, S., Ellul, C., Boudhaim, A.,
Exploring Schema Matching To Compare Geospatial Standards: Application To Underground Utility Networks,
DOI Link 1901

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Through the Wall Imaging, Radar, Microwave Imaging .

Last update:Jul 13, 2024 at 15:27:21