{"id":"https://openalex.org/W7129017929","doi":"https://doi.org/10.48550/arxiv.2602.13022","title":"Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels","display_name":"Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels","publication_year":2026,"publication_date":"2026-02-13","ids":{"openalex":"https://openalex.org/W7129017929","doi":"https://doi.org/10.48550/arxiv.2602.13022"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.13022","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13022","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.13022","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113192980","display_name":"Julius Pesonen","orcid":"https://orcid.org/0009-0000-9175-7129"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pesonen, Julius","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092490921","display_name":"Stefan Rua","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rua, Stefan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126084278","display_name":"Josef Taher","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taher, Josef","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037907825","display_name":"Niko Koivum\u00e4ki","orcid":"https://orcid.org/0000-0002-6307-1637"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koivum\u00e4ki, Niko","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126075704","display_name":"Xiaowei Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Xiaowei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010833142","display_name":"Eija Honkavaara","orcid":"https://orcid.org/0000-0002-7236-2145"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Honkavaara, Eija","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5113192980"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9879000186920166,"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":0.9879000186920166,"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":0.004800000227987766,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6729999780654907},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.6316999793052673},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.5467000007629395},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.531499981880188},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4341000020503998},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.430400013923645},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.39989998936653137},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.38929998874664307}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7382000088691711},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6729999780654907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6646000146865845},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.6316999793052673},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.531499981880188},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47690001130104065},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4341000020503998},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.430400013923645},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C2778400979","wikidata":"https://www.wikidata.org/wiki/Q143720","display_name":"Crown (dentistry)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.3393999934196472},{"id":"https://openalex.org/C141349535","wikidata":"https://www.wikidata.org/wiki/Q1361664","display_name":"Laser scanning","level":3,"score":0.311599999666214},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.310699999332428},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26420000195503235},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C60478076","wikidata":"https://www.wikidata.org/wiki/Q3036835","display_name":"Reference data","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.13022","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13022","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.13022","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13022","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5819423198699951,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Mapping":[0],"individual":[1,68],"tree":[2,12,49],"crowns":[3,31],"is":[4,38],"essential":[5],"for":[6,24,116,138],"tasks":[7],"such":[8,43],"as":[9,44],"maintaining":[10],"urban":[11],"inventories":[13],"and":[14,22,47,66,72],"monitoring":[15],"forest":[16],"health,":[17],"which":[18,129,134],"help":[19],"us":[20],"understand":[21],"care":[23],"our":[25],"environment.":[26],"However,":[27],"automatically":[28],"separating":[29],"the":[30,45,88,143],"from":[32,70,78],"each":[33],"other":[34],"in":[35],"aerial":[36,79],"imagery":[37],"challenging":[39],"due":[40],"to":[41,59,111,126],"factors":[42],"texture":[46],"partial":[48],"crown":[50],"overlaps.":[51],"In":[52],"this":[53],"study,":[54],"we":[55],"present":[56],"a":[57,95,109],"method":[58,107],"train":[60],"deep":[61],"learning":[62],"models":[63,119,128,133],"that":[64,87],"segment":[65],"separate":[67],"trees":[69],"RGB":[71],"multispectral":[73],"images,":[74],"using":[75,94],"pseudo-labels":[76,90],"derived":[77],"laser":[80],"scanning":[81],"(ALS)":[82],"data.":[83],"Our":[84,106],"study":[85],"shows":[86],"ALS-derived":[89],"can":[91],"be":[92],"enhanced":[93],"zero-shot":[96],"instance":[97],"segmentation":[98,127],"model,":[99],"Segment":[100],"Anything":[101],"Model":[102],"2":[103],"(SAM":[104],"2).":[105],"offers":[108],"way":[110],"obtain":[112],"domain-specific":[113],"training":[114],"annotations":[115],"optical":[117],"image-based":[118],"without":[120],"any":[121,131],"manual":[122],"annotation":[123],"cost,":[124],"leading":[125],"outperform":[130],"available":[132],"have":[135],"been":[136],"targeted":[137],"general":[139],"domain":[140],"deployment":[141],"on":[142],"same":[144],"task.":[145]},"counts_by_year":[],"updated_date":"2026-02-17T06:10:05.244387","created_date":"2026-02-17T00:00:00"}
