23.8.6.9.4 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
BibRef

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.
IEEE DOI 0808
BibRef

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.
IEEE DOI 0907
BibRef

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.
IEEE DOI 1203
BibRef

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.
IEEE DOI 1801
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.
IEEE DOI 1806
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.[Niccolň], 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
BibRef

Hu, Z.[Zihe], Guo, J.[Jing], Zhang, X.[Xuequan],
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 2108
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
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,
WACV21(266-275)
IEEE DOI 2106
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),
GeoInfo20(75-81).
DOI Link 2012
BibRef

Métral, C., Daponte, V., Caselli, A., di Marzo, G., Falquet, G.,
Ontology-based Rule Compliance Checking for Subsurface Objects,
GeoInfo20(91-94).
DOI Link 2012
BibRef

Yan, J., Jaw, S.W., Soon, K.H., Schrotter, G.,
The LADM-Based 3D Underground Utility Mapping: Case Study in Singapore,
GeoInfo19(117-122).
DOI Link 1912
BibRef

van Son, R., Jaw, S.W., Wieser, A.,
A Data Capture Framework for Improving The Quality of Subsurface Utility Information,
GeoInfo19(97-104).
DOI Link 1912
BibRef

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,
GeoInfo17(43-48).
DOI Link 1805
BibRef

Cazzaniga, N.E., Pagliari, D., Pinto, L.,
Photogrammetry For Mapping Underground Utility Lines With Ground Penetrating Radar In Urban Areas,
ISPRS12(XXXIX-B1:297-302).
DOI Link 1209
BibRef

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,
GeoInfo18(209-214).
DOI Link 1901
BibRef

Pouliot, J., Larrivée, S., Ellul, C., Boudhaim, A.,
Exploring Schema Matching To Compare Geospatial Standards: Application To Underground Utility Networks,
GeoInfo18(157-164).
DOI Link 1901
BibRef

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


Last update:Sep 19, 2021 at 21:11:01