{"id":"https://openalex.org/W7131830512","doi":"https://doi.org/10.1109/sii64115.2026.11404595","title":"Tree Canopy Segmentation and Characterization Using LiDAR for Machine Learning Models","display_name":"Tree Canopy Segmentation and Characterization Using LiDAR for Machine Learning Models","publication_year":2026,"publication_date":"2026-01-11","ids":{"openalex":"https://openalex.org/W7131830512","doi":"https://doi.org/10.1109/sii64115.2026.11404595"},"language":null,"primary_location":{"id":"doi:10.1109/sii64115.2026.11404595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sii64115.2026.11404595","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/SICE International Symposium on System Integration (SII)","raw_type":"proceedings-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/A5127360820","display_name":"Jes\u00fas A. Monroy-Anieva","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117728","display_name":"Center For Remote Sensing (United States)","ror":"https://ror.org/021wvg932","country_code":"US","type":"company","lineage":["https://openalex.org/I4210117728"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jes\u00fas A. Monroy-Anieva","raw_affiliation_strings":["Remote Sensing of Center for Research in Geospatial Information Sciences,Merida-Yuc.,Mexico,CP. 97302"],"affiliations":[{"raw_affiliation_string":"Remote Sensing of Center for Research in Geospatial Information Sciences,Merida-Yuc.,Mexico,CP. 97302","institution_ids":["https://openalex.org/I4210117728"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053984366","display_name":"Alejandro T\u00e9llez-Qui\u00f1ones","orcid":"https://orcid.org/0000-0001-6712-0582"},"institutions":[{"id":"https://openalex.org/I4210117728","display_name":"Center For Remote Sensing (United States)","ror":"https://ror.org/021wvg932","country_code":"US","type":"company","lineage":["https://openalex.org/I4210117728"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alejandro T\u00e9llez-Qui\u00f1ones","raw_affiliation_strings":["Remote Sensing of Center for Research in Geospatial Information Sciences,Merida-Yuc.,Mexico,CP. 97302"],"affiliations":[{"raw_affiliation_string":"Remote Sensing of Center for Research in Geospatial Information Sciences,Merida-Yuc.,Mexico,CP. 97302","institution_ids":["https://openalex.org/I4210117728"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127231893","display_name":"Rodrigo Lopez-Far\u00edas","orcid":null},"institutions":[{"id":"https://openalex.org/I4210120373","display_name":"Geospatial Research (United Kingdom)","ror":"https://ror.org/02qq5pa23","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210120373"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Rodrigo Lopez-Far\u00edas","raw_affiliation_strings":["Artificial Intelligence of Center for Research in Geospatial Information Sciences,Queretaro-Qro.,Mexico,CP. 76703"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence of Center for Research in Geospatial Information Sciences,Queretaro-Qro.,Mexico,CP. 76703","institution_ids":["https://openalex.org/I4210120373"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034066619","display_name":"Hip\u00f3lito Aguilar Sierra","orcid":"https://orcid.org/0000-0002-7049-1584"},"institutions":[{"id":"https://openalex.org/I4210149727","display_name":"Universidad La Salle","ror":"https://ror.org/05c99rg80","country_code":"MX","type":"education","lineage":["https://openalex.org/I4210149727"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Hip\u00f3lito Aguilar-Sierra","raw_affiliation_strings":["Universidad La Salle,Engineering Department,Mexico City,Mexico,CP. 06140"],"affiliations":[{"raw_affiliation_string":"Universidad La Salle,Engineering Department,Mexico City,Mexico,CP. 06140","institution_ids":["https://openalex.org/I4210149727"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5127360820"],"corresponding_institution_ids":["https://openalex.org/I4210117728"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.92264123,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1399","last_page":"1406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9391000270843506,"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":0.9391000270843506,"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":0.008200000040233135,"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/T10266","display_name":"Plant Water Relations and Carbon Dynamics","score":0.0034000000450760126,"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/tree","display_name":"Tree (set theory)","score":0.5595999956130981},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5394999980926514},{"id":"https://openalex.org/keywords/canopy","display_name":"Canopy","score":0.5256999731063843},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4690999984741211},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4555000066757202},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.43869999051094055},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.40880000591278076},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3946000039577484}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5692999958992004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5630999803543091},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5595999956130981},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5529000163078308},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5394999980926514},{"id":"https://openalex.org/C101000010","wikidata":"https://www.wikidata.org/wiki/Q5033434","display_name":"Canopy","level":2,"score":0.5256999731063843},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4690999984741211},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4555000066757202},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4512999951839447},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.43869999051094055},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C39807119","wikidata":"https://www.wikidata.org/wiki/Q1134228","display_name":"Tree canopy","level":3,"score":0.3643999993801117},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C181843262","wikidata":"https://www.wikidata.org/wiki/Q640492","display_name":"Digital elevation model","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3188000023365021},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C56289965","wikidata":"https://www.wikidata.org/wiki/Q5249246","display_name":"Decision tree model","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sii64115.2026.11404595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sii64115.2026.11404595","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/SICE International Symposium on System Integration (SII)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1980046609","https://openalex.org/W2028427954","https://openalex.org/W2043047386","https://openalex.org/W2090982672","https://openalex.org/W2137882837","https://openalex.org/W2165148193","https://openalex.org/W2279566557","https://openalex.org/W2296685749","https://openalex.org/W2315994349","https://openalex.org/W2473991816","https://openalex.org/W2601849987","https://openalex.org/W2763287719","https://openalex.org/W2765920222","https://openalex.org/W2787518617","https://openalex.org/W2794911584","https://openalex.org/W2802098100","https://openalex.org/W2811093923","https://openalex.org/W2892336504","https://openalex.org/W2901497416","https://openalex.org/W2938586265","https://openalex.org/W2950604226","https://openalex.org/W3012494314","https://openalex.org/W3024297428","https://openalex.org/W3036406207","https://openalex.org/W3205337742","https://openalex.org/W4226434098","https://openalex.org/W4280496735","https://openalex.org/W4294118512","https://openalex.org/W4311528296","https://openalex.org/W4313898799","https://openalex.org/W4390122887","https://openalex.org/W4392806007","https://openalex.org/W4396213369","https://openalex.org/W4398761945","https://openalex.org/W4399232247","https://openalex.org/W4399350278","https://openalex.org/W4403429382","https://openalex.org/W4403649439","https://openalex.org/W4406962125","https://openalex.org/W7125803329"],"related_works":[],"abstract_inverted_index":{"This":[0,33],"work":[1],"proposes":[2],"an":[3],"alternative":[4],"solution":[5],"to":[6,56,105,189],"address":[7],"the":[8,21,41,62,91,101,123,131,143,194,202],"problem":[9],"of":[10,20,44,61,90,100,119,148],"tree":[11,50,63,71,112],"canopy":[12,51,99,113,140],"classification,":[13,141],"given":[14],"a":[15,26,36,58,70,107,168],"digital":[16],"elevation":[17],"model":[18],"(DEM)":[19],"study":[22],"area":[23],"obtained":[24],"using":[25,69,130],"Light":[27],"Detection":[28],"and":[29,74,81,87,128,156,171,180,201,211,219],"Ranging":[30],"(LiDAR)":[31],"device.":[32],"proposal":[34],"presents":[35],"comprehensive":[37],"methodology":[38,187],"that":[39],"enables":[40],"feasible":[42],"application":[43],"machine":[45],"learning":[46],"(ML)":[47],"models":[48,149],"for":[49,96,216],"classification.":[52],"Our":[53],"approach":[54],"allows":[55],"obtain":[57],"composite":[59],"DEM":[60,102],"canopy,":[64],"which":[65,109],"will":[66],"be":[67],"characterized":[68],"cover":[72],"selection":[73],"extraction":[75],"strategy":[76],"based":[77,150],"on":[78,151],"Euclidean":[79],"distance":[80],"feature":[82],"point":[83,92],"clustering.":[84],"Subsequently,":[85],"statistical":[86],"geometric":[88],"measures":[89],"clouds":[93],"are":[94],"calculated":[95],"each":[97],"segmented":[98],"in":[103,139,145],"order":[104],"prepare":[106],"dataset,":[108],"includes":[110],"some":[111],"features":[114],"represented":[115],"by":[116,174],"column":[117],"vectors":[118],"77":[120],"predictors.":[121],"Finally,":[122],"generated":[124],"dataset":[125],"was":[126],"trained":[127],"validated":[129],"MatLab\u2019s":[132],"classification":[133,146],"learner,":[134],"obtaining":[135],"considerable":[136],"performance":[137,147],"results":[138],"highlighting":[142],"improvement":[144],"Naive":[152],"Bayes,":[153],"Neural":[154],"Networks,":[155],"Support":[157],"Vector":[158],"Machines.":[159],"Furthermore,":[160],"beyond":[161],"its":[162],"technical":[163],"scope,":[164],"this":[165],"research":[166],"has":[167],"direct":[169],"social":[170,220],"environmental":[172],"impact":[173],"promoting":[175],"urban":[176],"resilience,":[177],"biodiversity":[178],"conservation,":[179],"strengthening":[181],"sustainable":[182],"development":[183],"policies.":[184],"The":[185],"proposed":[186],"contributes":[188],"global":[190],"agendas":[191],"such":[192],"as":[193,214],"United":[195],"Nations":[196],"Sustainable":[197],"Development":[198],"Goals":[199],"(SDGs)":[200],"Mexican":[203],"National":[204],"Strategic":[205],"Programs":[206],"(PRONACES),":[207],"positioning":[208],"remote":[209],"sensing":[210],"artificial":[212],"intelligence":[213],"tools":[215],"climate":[217],"action":[218],"well-being.":[221]},"counts_by_year":[],"updated_date":"2026-03-01T06:05:34.837733","created_date":"2026-02-28T00:00:00"}