{"id":"https://openalex.org/W3096045206","doi":"https://doi.org/10.1109/tgrs.2020.3030079","title":"Mapping Subsurface Utility Pipes by 3-D Convolutional Neural Network and Kirchhoff Migration Using GPR Images","display_name":"Mapping Subsurface Utility Pipes by 3-D Convolutional Neural Network and Kirchhoff Migration Using GPR Images","publication_year":2020,"publication_date":"2020-10-29","ids":{"openalex":"https://openalex.org/W3096045206","doi":"https://doi.org/10.1109/tgrs.2020.3030079","mag":"3096045206"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2020.3030079","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3030079","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046894215","display_name":"Takahiro Yamaguchi","orcid":"https://orcid.org/0000-0002-2145-2415"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takahiro Yamaguchi","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024399662","display_name":"Tsukasa Mizutani","orcid":"https://orcid.org/0000-0002-4275-7832"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tsukasa Mizutani","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019721231","display_name":"Tomonori Nagayama","orcid":"https://orcid.org/0000-0003-1387-4738"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomonori Nagayama","raw_affiliation_strings":["Graduate School of Engineering, The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046894215"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":5.778,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.96402097,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"59","issue":"8","first_page":"6525","last_page":"6536"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11757","display_name":"Seismic Waves and Analysis","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ground-penetrating-radar","display_name":"Ground-penetrating radar","score":0.862555980682373},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7457069754600525},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.666888415813446},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5582616329193115},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4290747046470642},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.386238157749176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34725821018218994},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33900633454322815},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.1637077033519745}],"concepts":[{"id":"https://openalex.org/C71813955","wikidata":"https://www.wikidata.org/wiki/Q503560","display_name":"Ground-penetrating radar","level":3,"score":0.862555980682373},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7457069754600525},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.666888415813446},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5582616329193115},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4290747046470642},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.386238157749176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34725821018218994},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33900633454322815},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.1637077033519745},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2020.3030079","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3030079","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.699999988079071,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W595062818","https://openalex.org/W596928057","https://openalex.org/W626810539","https://openalex.org/W627907298","https://openalex.org/W639708223","https://openalex.org/W1417564892","https://openalex.org/W1532220468","https://openalex.org/W1536680647","https://openalex.org/W1608362678","https://openalex.org/W1686810756","https://openalex.org/W1964428975","https://openalex.org/W1973352451","https://openalex.org/W1983364832","https://openalex.org/W1986081346","https://openalex.org/W1994932083","https://openalex.org/W2003328900","https://openalex.org/W2033496795","https://openalex.org/W2094183534","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2097778784","https://openalex.org/W2102566310","https://openalex.org/W2123665156","https://openalex.org/W2126789657","https://openalex.org/W2130976145","https://openalex.org/W2138489340","https://openalex.org/W2141014056","https://openalex.org/W2151141851","https://openalex.org/W2163605009","https://openalex.org/W2164663617","https://openalex.org/W2193145675","https://openalex.org/W2301358467","https://openalex.org/W2327500767","https://openalex.org/W2395626468","https://openalex.org/W2467399594","https://openalex.org/W2518909974","https://openalex.org/W2619777454","https://openalex.org/W2791164041","https://openalex.org/W2891460087","https://openalex.org/W2963037989","https://openalex.org/W3106250896","https://openalex.org/W3149976863","https://openalex.org/W4247034414","https://openalex.org/W4376497051","https://openalex.org/W6637373629","https://openalex.org/W6674330103","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W4315471419","https://openalex.org/W2946057701","https://openalex.org/W4386931161","https://openalex.org/W2374146176","https://openalex.org/W2065249286","https://openalex.org/W2366839571","https://openalex.org/W4223960160","https://openalex.org/W2027762722","https://openalex.org/W2356754952","https://openalex.org/W3090858966"],"abstract_inverted_index":{"In":[0,46],"this":[1,47],"article,":[2,48],"we":[3,49],"focus":[4],"on":[5],"ground-penetrating":[6],"radar":[7,33],"(GPR)":[8],"for":[9],"subsurface":[10],"utility":[11],"pipe":[12],"detection.":[13],"Due":[14],"to":[15,106,131],"the":[16,44,56,83,91,99,109,116,120,140,146,149],"dense":[17],"and":[18,35,41,64,74,94,135,156],"high-speed":[19],"3-D":[20,59,117,154],"monitoring,":[21],"GPR":[22],"is":[23],"a":[24,51],"promising":[25],"tool.":[26],"However,":[27],"because":[28],"of":[29,32,37,58,82,111,119,142,152],"enormous":[30],"amount":[31],"data":[34],"difficulty":[36],"interpretation,":[38],"inspection":[39],"time":[40],"cost":[42],"are":[43],"bottlenecks.":[45],"propose":[50],"novel":[52],"detection":[53],"algorithm":[54,147],"by":[55,102,114,125],"combination":[57],"convolutional":[60],"neural":[61],"network":[62],"(3-D-CNN)":[63],"Kirchhoff":[65,127],"migration.":[66],"A":[67],"3-D-CNN":[68,97],"architecture":[69],"was":[70,86,129],"trained":[71],"utilizing":[72],"transverse":[73,112],"longitudinal":[75],"pipes\u2019":[76,92,153],"measurement":[77],"data.":[78],"The":[79,96],"classification":[80,100],"accuracy":[81,101],"developed":[84],"model":[85],"about":[87,103],"91%,":[88],"accurately":[89],"estimating":[90],"existences":[93],"directions.":[95],"improved":[98],"6%":[104],"compared":[105],"2-D-CNN":[107],"in":[108],"case":[110],"pipes":[113],"considering":[115],"geometries":[118],"pipes.":[121],"After":[122],"box-by-box":[123],"search":[124],"3-D-CNN,":[126],"migration":[128],"applied":[130],"cross":[132],"section":[133],"images":[134],"peaks":[136],"were":[137],"extracted.":[138],"From":[139],"result":[141],"experimental":[143],"field":[144],"data,":[145],"provides":[148],"clear":[150],"understandings":[151],"positions":[155],"arrangement":[157],"with":[158],"reasonable":[159],"calculation":[160],"time.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
