{"id":"https://openalex.org/W3174230884","doi":"https://doi.org/10.1109/jstars.2021.3091389","title":"A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification","display_name":"A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3174230884","doi":"https://doi.org/10.1109/jstars.2021.3091389","mag":"3174230884"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2021.3091389","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3091389","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09462932.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09462932.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100341048","display_name":"Nan Li","orcid":"https://orcid.org/0000-0002-2375-8098"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Nan Li","raw_affiliation_strings":["Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria"],"raw_orcid":"https://orcid.org/0000-0002-2375-8098","affiliations":[{"raw_affiliation_string":"Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028438329","display_name":"Olaf K\u00e4hler","orcid":null},"institutions":[{"id":"https://openalex.org/I4210127033","display_name":"Siemens (Austria)","ror":"https://ror.org/03794w632","country_code":"AT","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210127033"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Olaf Kahler","raw_affiliation_strings":["Research Group Active Vision Technologies, Siemens AG Österreich, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Group Active Vision Technologies, Siemens AG Österreich, Vienna, Austria","institution_ids":["https://openalex.org/I4210127033"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081275712","display_name":"Norbert Pfeifer","orcid":"https://orcid.org/0000-0002-2348-7929"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Norbert Pfeifer","raw_affiliation_strings":["Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria"],"raw_orcid":"https://orcid.org/0000-0002-2348-7929","affiliations":[{"raw_affiliation_string":"Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1301,"currency":"EUR","value_usd":1403},"fwci":2.6553,"has_fulltext":true,"cited_by_count":38,"citation_normalized_percentile":{"value":0.89402715,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"14","issue":null,"first_page":"6467","last_page":"6486"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"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/T10266","display_name":"Plant Water Relations and Carbon Dynamics","score":0.9830999970436096,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.799728274345398},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7780323028564453},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7350963950157166},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6772856116294861},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5906226634979248},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5160341262817383},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48954257369041443},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.47103267908096313},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.44049137830734253},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4322248697280884},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40926969051361084},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23161101341247559},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.15371966361999512},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10296332836151123},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07597297430038452}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.799728274345398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7780323028564453},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7350963950157166},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6772856116294861},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5906226634979248},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5160341262817383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48954257369041443},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.47103267908096313},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.44049137830734253},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4322248697280884},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40926969051361084},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23161101341247559},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.15371966361999512},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10296332836151123},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07597297430038452},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2021.3091389","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3091389","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09462932.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b2a77a8b22d44a8e8bd0597006a797e0","is_oa":true,"landing_page_url":"https://doaj.org/article/b2a77a8b22d44a8e8bd0597006a797e0","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 6467-6486 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2021.3091389","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3091389","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09462932.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322839","display_name":"Technische Universit\u00e4t Wien","ror":"https://ror.org/04d836q62"},{"id":"https://openalex.org/F4320335937","display_name":"Technische Universit\u00e4t Wien Bibliothek","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3174230884.pdf","grobid_xml":"https://content.openalex.org/works/W3174230884.grobid-xml"},"referenced_works_count":94,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1966438710","https://openalex.org/W1973644502","https://openalex.org/W1981994935","https://openalex.org/W1985908905","https://openalex.org/W2011287807","https://openalex.org/W2040861824","https://openalex.org/W2061064077","https://openalex.org/W2076280802","https://openalex.org/W2102673432","https://openalex.org/W2114892120","https://openalex.org/W2115703234","https://openalex.org/W2126786328","https://openalex.org/W2129725504","https://openalex.org/W2131256165","https://openalex.org/W2154772499","https://openalex.org/W2170646669","https://openalex.org/W2190691619","https://openalex.org/W2211722331","https://openalex.org/W2264432461","https://openalex.org/W2415334435","https://openalex.org/W2552085615","https://openalex.org/W2556802233","https://openalex.org/W2560609797","https://openalex.org/W2564924987","https://openalex.org/W2594519801","https://openalex.org/W2609077090","https://openalex.org/W2609747629","https://openalex.org/W2737886371","https://openalex.org/W2761129952","https://openalex.org/W2772515592","https://openalex.org/W2788158258","https://openalex.org/W2797997528","https://openalex.org/W2810641456","https://openalex.org/W2902302021","https://openalex.org/W2912581782","https://openalex.org/W2919824850","https://openalex.org/W2946991328","https://openalex.org/W2950392396","https://openalex.org/W2954258401","https://openalex.org/W2960986959","https://openalex.org/W2962701877","https://openalex.org/W2962838801","https://openalex.org/W2962928871","https://openalex.org/W2963121255","https://openalex.org/W2963158438","https://openalex.org/W2963182550","https://openalex.org/W2963231572","https://openalex.org/W2963281829","https://openalex.org/W2963509914","https://openalex.org/W2963719584","https://openalex.org/W2964251415","https://openalex.org/W2964253930","https://openalex.org/W2968622120","https://openalex.org/W2979750740","https://openalex.org/W2990613095","https://openalex.org/W2996167479","https://openalex.org/W3007809903","https://openalex.org/W3008274274","https://openalex.org/W3012494314","https://openalex.org/W3016245793","https://openalex.org/W3025468529","https://openalex.org/W3025802147","https://openalex.org/W3034239841","https://openalex.org/W3034981362","https://openalex.org/W3040741167","https://openalex.org/W3041273049","https://openalex.org/W3104568842","https://openalex.org/W3107479685","https://openalex.org/W3109154950","https://openalex.org/W3113211818","https://openalex.org/W3119816767","https://openalex.org/W3127091182","https://openalex.org/W3137210930","https://openalex.org/W3181190968","https://openalex.org/W4244565819","https://openalex.org/W4287554952","https://openalex.org/W4393787006","https://openalex.org/W4394671432","https://openalex.org/W6637887740","https://openalex.org/W6639824700","https://openalex.org/W6687484953","https://openalex.org/W6739778489","https://openalex.org/W6747904511","https://openalex.org/W6750189863","https://openalex.org/W6753266022","https://openalex.org/W6756243994","https://openalex.org/W6763422710","https://openalex.org/W6765299845","https://openalex.org/W6780045877","https://openalex.org/W6781600403","https://openalex.org/W6786618451","https://openalex.org/W6788009311","https://openalex.org/W6863531621"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020","https://openalex.org/W1964041166","https://openalex.org/W4390887692","https://openalex.org/W4293094720","https://openalex.org/W2739701376"],"abstract_inverted_index":{"The":[0,62],"success":[1],"achieved":[2],"by":[3,141],"deep":[4,17,30,50,67],"learning":[5,18,31,51,68],"techniques":[6],"in":[7,15],"image":[8],"labeling":[9],"has":[10],"triggered":[11],"a":[12,44,109,132],"growing":[13],"interest":[14],"applying":[16],"for":[19],"three-dimensional":[20],"point":[21,38],"cloud":[22,39],"classification.":[23],"To":[24],"provide":[25],"better":[26,111,133],"insights":[27],"into":[28],"different":[29,59,143],"architectures":[32],"and":[33,55,95,117,124,137],"their":[34],"applications":[35],"to":[36,84,108,115,135],"ALS":[37,60],"classification,":[40],"this":[41],"article":[42],"presents":[43],"comprehensive":[45],"comparison":[46],"among":[47],"three":[48,66],"state-of-the-art":[49],"networks:":[52],"PointNet++,":[53,93],"SparseCNN,":[54,94],"KPConv,":[56,118],"on":[57,101],"two":[58],"datasets.":[61],"performances":[63],"of":[64,87,145],"these":[65],"networks":[69],"are":[70],"compared":[71,114],"w.r.t.":[72],"classification":[73,103,112],"accuracy,":[74],"computation":[75,122],"time,":[76,129],"generalization":[77],"ability":[78,134],"as":[79,81],"well":[80],"the":[82,85,102,127,142],"sensitivity":[83],"choices":[86,144],"hyper-parameters.":[88,146],"Overall,":[89],"we":[90],"observed":[91],"that":[92],"KPConv":[96],"all":[97],"outperform":[98],"Random":[99],"Forest":[100],"results.":[104],"Moreover,":[105],"SparseCNN":[106],"leads":[107],"slightly":[110],"result":[113],"PointNet++":[116],"while":[119],"requiring":[120],"less":[121,139],"time":[123],"memory.":[125],"At":[126],"same":[128],"it":[130],"shows":[131],"generalize":[136],"is":[138],"impacted":[140]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}