{"id":"https://openalex.org/W4285133154","doi":"https://doi.org/10.1109/lgrs.2022.3181680","title":"An Approach Based on Deep Learning for Tree Species Classification in LiDAR Data Acquired in Mixed Forest","display_name":"An Approach Based on Deep Learning for Tree Species Classification in LiDAR Data Acquired in Mixed Forest","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285133154","doi":"https://doi.org/10.1109/lgrs.2022.3181680"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2022.3181680","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3181680","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":true,"oa_status":"green","oa_url":"https://ris.utwente.nl/ws/files/287346736/Marinelli_2022_An_approach_based_on_deep_learning_.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018193287","display_name":"Daniele Marinelli","orcid":"https://orcid.org/0000-0002-5038-6373"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Daniele Marinelli","raw_affiliation_strings":["Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"],"raw_orcid":"https://orcid.org/0000-0002-5038-6373","affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008344573","display_name":"Claudia Paris","orcid":"https://orcid.org/0000-0002-7189-6268"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Claudia Paris","raw_affiliation_strings":["Department of Natural Resources ITC, University of Twente, NB Enschede, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-7189-6268","affiliations":[{"raw_affiliation_string":"Department of Natural Resources ITC, University of Twente, NB Enschede, The Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006095323","display_name":"Lorenzo Bruzzone","orcid":"https://orcid.org/0000-0002-6036-459X"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Lorenzo Bruzzone","raw_affiliation_strings":["Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"],"raw_orcid":"https://orcid.org/0000-0002-6036-459X","affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4717,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.78914153,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":1.0,"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":1.0,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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":0.9925000071525574,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.8868192434310913},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7326259613037109},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.6357581615447998},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.62893146276474},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6030936241149902},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.49359428882598877},{"id":"https://openalex.org/keywords/crown","display_name":"Crown (dentistry)","score":0.4651741683483124},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4506254494190216},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4259449541568756},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37023675441741943},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17818188667297363},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15306514501571655}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8868192434310913},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7326259613037109},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.6357581615447998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.62893146276474},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6030936241149902},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.49359428882598877},{"id":"https://openalex.org/C2778400979","wikidata":"https://www.wikidata.org/wiki/Q143720","display_name":"Crown (dentistry)","level":2,"score":0.4651741683483124},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4506254494190216},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4259449541568756},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37023675441741943},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17818188667297363},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15306514501571655},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199343813","wikidata":"https://www.wikidata.org/wiki/Q12128","display_name":"Dentistry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/lgrs.2022.3181680","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3181680","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"},{"id":"pmh:oai:ris.utwente.nl:publications/584b3840-2ed5-4773-aae0-a6771047407c","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/584b3840-2ed5-4773-aae0-a6771047407c","pdf_url":"https://ris.utwente.nl/ws/files/287346736/Marinelli_2022_An_approach_based_on_deep_learning_.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Marinelli, D, Paris, C & Bruzzone, L 2022, 'An approach based on Deep Learning for tree species classification in LiDAR data acquired in mixed forest', IEEE geoscience and remote sensing letters, vol. 19, 7004305. https://doi.org/10.1109/LGRS.2022.3181680","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:iris.unitn.it:11572/364396","is_oa":false,"landing_page_url":"https://hdl.handle.net/11572/364396","pdf_url":null,"source":{"id":"https://openalex.org/S4306401913","display_name":"Institutional Research Information System (Universit\u00e0 degli Studi di Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:ris.utwente.nl:publications/584b3840-2ed5-4773-aae0-a6771047407c","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/584b3840-2ed5-4773-aae0-a6771047407c","pdf_url":"https://ris.utwente.nl/ws/files/287346736/Marinelli_2022_An_approach_based_on_deep_learning_.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Marinelli, D, Paris, C & Bruzzone, L 2022, 'An approach based on Deep Learning for tree species classification in LiDAR data acquired in mixed forest', IEEE geoscience and remote sensing letters, vol. 19, 7004305. https://doi.org/10.1109/LGRS.2022.3181680","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.46000000834465027}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332478","display_name":"U.S. Forest Service","ror":"https://ror.org/03zmjc935"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4285133154.pdf","grobid_xml":"https://content.openalex.org/works/W4285133154.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W1644641054","https://openalex.org/W1652321627","https://openalex.org/W1686810756","https://openalex.org/W2055734610","https://openalex.org/W2181716224","https://openalex.org/W2588926339","https://openalex.org/W2618530766","https://openalex.org/W2767814567","https://openalex.org/W2963121255","https://openalex.org/W2987303464","https://openalex.org/W3121749275","https://openalex.org/W3136555145","https://openalex.org/W6637373629","https://openalex.org/W6739778489"],"related_works":["https://openalex.org/W3080305507","https://openalex.org/W4214729122","https://openalex.org/W2391506322","https://openalex.org/W2189250119","https://openalex.org/W2272572439","https://openalex.org/W2746940507","https://openalex.org/W3096213782","https://openalex.org/W2030080266","https://openalex.org/W1561699119","https://openalex.org/W1882707605"],"abstract_inverted_index":{"This":[0,95,135],"letter":[1],"proposes":[2],"a":[3,85,120,149,169,200,203],"novel":[4],"method":[5,183,219],"based":[6],"on":[7,40,47,139,161],"Deep":[8],"Learning":[9],"(DL)":[10],"to":[11,43,51,61,83,129,194,199,212,237],"forest":[12,42,63,172],"species":[13,206],"classification":[14,207],"in":[15],"airborne":[16,164],"Light":[17],"Detection":[18],"and":[19,56,102,115,177,196,202,226,230,232,240],"Ranging":[20],"(LiDAR)":[21],"data.":[22,156],"Differently":[23],"from":[24],"the":[25,29,41,48,53,69,89,93,98,100,103,106,113,131,140,185,189,217,238],"state-of-":[26],"the-art":[27],"approaches,":[28],"proposed":[30,182,218],"method:":[31],"(1)":[32],"does":[33],"not":[34],"assume":[35],"any":[36],"prior":[37],"knowledge":[38],"either":[39],"be":[44,59],"classified":[45],"or":[46,214],"sensor":[49],"used":[50,128],"acquire":[52],"LiDAR":[54,165],"data,":[55],"(2)":[57],"can":[58],"applied":[60,211],"heterogeneous":[62],"characterized":[64,173],"by":[65,174],"mixed":[66],"species.":[67,134,180],"First,":[68],"3D":[70],"point":[71],"cloud":[72],"of":[73,88,92,105,153,223],"each":[74],"individual":[75],"tree":[76,107,133,205],"is":[77,137],"decomposed":[78],"into":[79],"8":[80],"angular":[81],"sectors":[82],"generate":[84],"multi-slices":[86],"representation":[87,96],"vertical":[90],"structure":[91],"tree.":[94],"models":[97],"foliage,":[99],"stem":[101],"branches":[104],"crown":[108,117],"as":[109,111],"well":[110],"depicts":[112],"internal":[114],"external":[116],"properties.":[118],"Then,":[119],"Multi-View":[121],"CNN":[122],"(MVCNN)":[123],"DL":[124,201,239],"automatically":[125],"extracts":[126],"features":[127],"discriminate":[130],"different":[132],"network":[136],"pre-trained":[138],"massive":[141],"ImageNet":[142],"database,":[143],"thus":[144],"guaranteeing":[145],"fast":[146],"convergence":[147],"with":[148],"relatively":[150],"small":[151],"number":[152],"ground":[154],"reference":[155],"Experiments":[157],"were":[158],"carried":[159],"out":[160],"high":[162],"density":[163],"data":[166],"collected":[167],"over":[168],"multi-layer":[170],"multi-age":[171],"four":[175],"conifers":[176],"three":[178],"broadleaf":[179],"The":[181],"outperformed":[184],"state-of-the-art":[186],"approaches":[187],"increasing":[188],"Overall":[190],"Accuracy":[191],"(OA)":[192],"up":[193],"16%":[195],"18.9%":[197],"compared":[198,236],"shallow":[204,241],"methods,":[208,242],"respectively.":[209,243],"When":[210],"coniferous":[213],"broadlaef":[215],"forests,":[216],"showed":[220],"an":[221],"increase":[222],"OA":[224],"10.1%":[225],"15.9%":[227],"(for":[228,234],"conifers),":[229],"9.5%":[231],"21.6%":[233],"broadleafs)":[235]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}