{"id":"https://openalex.org/W3043468555","doi":"https://doi.org/10.1109/lgrs.2020.3005209","title":"LiDAR Data Classification Based on Automatic Designed CNN","display_name":"LiDAR Data Classification Based on Automatic Designed CNN","publication_year":2020,"publication_date":"2020-07-13","ids":{"openalex":"https://openalex.org/W3043468555","doi":"https://doi.org/10.1109/lgrs.2020.3005209","mag":"3043468555"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3005209","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3005209","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5102857796","display_name":"Hao Xie","orcid":"https://orcid.org/0009-0001-2205-4603"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Xie","raw_affiliation_strings":["School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027034650","display_name":"Yushi Chen","orcid":"https://orcid.org/0000-0003-2421-0996"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yushi Chen","raw_affiliation_strings":["School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0003-2421-0996","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.5704,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.63616006,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"18","issue":"9","first_page":"1665","last_page":"1669"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7942668199539185},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7837444543838501},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.640170693397522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6144708395004272},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5843684673309326},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5466400384902954},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.49043580889701843},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4823826849460602},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.4644731283187866},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.4529399871826172},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.41590118408203125},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3367648124694824},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.31381022930145264},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.24881020188331604},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24680712819099426},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1004767119884491},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08050099015235901}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7942668199539185},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7837444543838501},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.640170693397522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6144708395004272},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5843684673309326},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5466400384902954},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.49043580889701843},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4823826849460602},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.4644731283187866},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.4529399871826172},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.41590118408203125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3367648124694824},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.31381022930145264},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.24881020188331604},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24680712819099426},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1004767119884491},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08050099015235901},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2020.3005209","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3005209","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G978713049","display_name":"\u9ad8\u5149\u8c31\u9065\u611f\u6570\u636e\u5f31\u76d1\u7763\u5206\u7c7b\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61971164","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1663973292","https://openalex.org/W1971796065","https://openalex.org/W1993826883","https://openalex.org/W2026596529","https://openalex.org/W2114892120","https://openalex.org/W2129725504","https://openalex.org/W2183341477","https://openalex.org/W2548655498","https://openalex.org/W2565258258","https://openalex.org/W2594913814","https://openalex.org/W2775040228","https://openalex.org/W2791011783","https://openalex.org/W2810075754","https://openalex.org/W2894895353","https://openalex.org/W2951104886","https://openalex.org/W2965658867","https://openalex.org/W2992308087","https://openalex.org/W4212863985","https://openalex.org/W6747806236","https://openalex.org/W6752515464","https://openalex.org/W6762226699"],"related_works":["https://openalex.org/W3026913501","https://openalex.org/W3036126916","https://openalex.org/W2428997408","https://openalex.org/W3012393889","https://openalex.org/W4210965063","https://openalex.org/W3166873558","https://openalex.org/W2742991909","https://openalex.org/W3081496756","https://openalex.org/W3206592002","https://openalex.org/W2969680539"],"abstract_inverted_index":{"Recently,":[0],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs)":[4],"have":[5],"been":[6],"widely":[7],"used":[8,67],"for":[9,23,49,53,113],"light":[10],"detection":[11],"and":[12,63,132,162],"ranging":[13],"(LiDAR)":[14],"data":[15,25,51,115,156,164],"classification.":[16,116],"Although":[17],"CNNs":[18],"achieve":[19],"good":[20],"classification":[21,52,141,148],"performance":[22,142],"LiDAR":[24,50,114,144,155],"classification,":[26],"a":[27,35,58,86,95,107,127],"lot":[28],"of":[29,45,85,143],"efforts":[30],"are":[31,135,150],"needed":[32],"to":[33,68,120,137,179],"design":[34,44],"proper":[36],"architecture.":[37],"In":[38],"this":[39],"letter,":[40],"the":[41,54,70,74,82,122,140,159,171,175,180],"automatic":[42],"modularized":[43],"CNN":[46,109],"is":[47,66,81,89],"explored":[48],"first":[55],"time.":[56],"First,":[57],"searchable":[59],"architecture":[60],"containing":[61],"convolution":[62],"pooling":[64],"operations":[65],"establish":[69],"search":[71,92],"space.":[72],"Then,":[73],"optimal":[75,104],"building":[76,105],"block":[77],"(i.e.,":[78,158],"cell),":[79],"which":[80],"basic":[83],"part":[84],"deep":[87,108],"CNN,":[88,128],"obtained":[90],"from":[91],"space":[93],"by":[94,101],"gradient":[96],"decent-based":[97],"algorithm.":[98],"At":[99],"last,":[100],"stacking":[102],"several":[103],"blocks,":[106],"can":[110],"be":[111],"formulated":[112],"Moreover,":[117],"in":[118,125],"order":[119],"mitigate":[121],"overfitting":[123],"problem":[124],"training":[126],"improved":[129],"label":[130],"smoothing":[131],"feature":[133],"regularization":[134],"proposed":[136,147,172],"further":[138],"improve":[139],"data.":[145],"The":[146,166],"models":[149,173],"evaluated":[151],"on":[152],"two":[153],"popular":[154],"sets":[157],"Bayview":[160],"Park":[161],"Houston":[163],"sets).":[165],"experimental":[167],"results":[168,177],"show":[169],"that":[170],"provide":[174],"competitive":[176],"compared":[178],"state-of-the-art":[181],"methods.":[182]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}