{"id":"https://openalex.org/W4220676290","doi":"https://doi.org/10.3390/rs14071561","title":"How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation","display_name":"How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation","publication_year":2022,"publication_date":"2022-03-24","ids":{"openalex":"https://openalex.org/W4220676290","doi":"https://doi.org/10.3390/rs14071561"},"language":"en","primary_location":{"id":"doi:10.3390/rs14071561","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14071561","pdf_url":"https://www.mdpi.com/2072-4292/14/7/1561/pdf?version=1648103622","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/14/7/1561/pdf?version=1648103622","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070039667","display_name":"Zhenbang Hao","orcid":"https://orcid.org/0000-0002-4094-7157"},"institutions":[{"id":"https://openalex.org/I61057504","display_name":"Fujian Agriculture and Forestry University","ror":"https://ror.org/04kx2sy84","country_code":"CN","type":"education","lineage":["https://openalex.org/I61057504"]},{"id":"https://openalex.org/I83791580","display_name":"Fujian University of Technology","ror":"https://ror.org/03c8fdb16","country_code":"CN","type":"education","lineage":["https://openalex.org/I83791580"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenbang Hao","raw_affiliation_strings":["College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China","University Key Lab for Geomatics Technology and Optimized Resources Utilization in Fujian Province, Fuzhou 350002, China"],"raw_orcid":"https://orcid.org/0000-0002-4094-7157","affiliations":[{"raw_affiliation_string":"College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China","institution_ids":["https://openalex.org/I61057504"]},{"raw_affiliation_string":"University Key Lab for Geomatics Technology and Optimized Resources Utilization in Fujian Province, Fuzhou 350002, China","institution_ids":["https://openalex.org/I83791580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109984776","display_name":"Christopher J. Post","orcid":null},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher J. Post","raw_affiliation_strings":["Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047303033","display_name":"Elena A. Mikhailova","orcid":"https://orcid.org/0000-0003-1711-7910"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elena A. Mikhailova","raw_affiliation_strings":["Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA"],"raw_orcid":"https://orcid.org/0000-0003-1711-7910","affiliations":[{"raw_affiliation_string":"Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080218546","display_name":"Lili Lin","orcid":"https://orcid.org/0000-0001-5098-8182"},"institutions":[{"id":"https://openalex.org/I9356336","display_name":"Minnan Normal University","ror":"https://ror.org/02vj1vm13","country_code":"CN","type":"education","lineage":["https://openalex.org/I9356336"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lili Lin","raw_affiliation_strings":["Department of Biological Science and Biotechnology, Minnan Normal University, Zhangzhou 363000, China"],"raw_orcid":"https://orcid.org/0000-0001-5098-8182","affiliations":[{"raw_affiliation_string":"Department of Biological Science and Biotechnology, Minnan Normal University, Zhangzhou 363000, China","institution_ids":["https://openalex.org/I9356336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031787246","display_name":"Jian Liu","orcid":"https://orcid.org/0000-0001-5329-7408"},"institutions":[{"id":"https://openalex.org/I61057504","display_name":"Fujian Agriculture and Forestry University","ror":"https://ror.org/04kx2sy84","country_code":"CN","type":"education","lineage":["https://openalex.org/I61057504"]},{"id":"https://openalex.org/I83791580","display_name":"Fujian University of Technology","ror":"https://ror.org/03c8fdb16","country_code":"CN","type":"education","lineage":["https://openalex.org/I83791580"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jian Liu","raw_affiliation_strings":["College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China","University Key Lab for Geomatics Technology and Optimized Resources Utilization in Fujian Province, Fuzhou 350002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China","institution_ids":["https://openalex.org/I61057504"]},{"raw_affiliation_string":"University Key Lab for Geomatics Technology and Optimized Resources Utilization in Fujian Province, Fuzhou 350002, China","institution_ids":["https://openalex.org/I83791580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065050708","display_name":"Kunyong Yu","orcid":"https://orcid.org/0000-0002-9584-8068"},"institutions":[{"id":"https://openalex.org/I61057504","display_name":"Fujian Agriculture and Forestry University","ror":"https://ror.org/04kx2sy84","country_code":"CN","type":"education","lineage":["https://openalex.org/I61057504"]},{"id":"https://openalex.org/I83791580","display_name":"Fujian University of Technology","ror":"https://ror.org/03c8fdb16","country_code":"CN","type":"education","lineage":["https://openalex.org/I83791580"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kunyong Yu","raw_affiliation_strings":["College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China","University Key Lab for Geomatics Technology and Optimized Resources Utilization in Fujian Province, Fuzhou 350002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China","institution_ids":["https://openalex.org/I61057504"]},{"raw_affiliation_string":"University Key Lab for Geomatics Technology and Optimized Resources Utilization in Fujian Province, Fuzhou 350002, China","institution_ids":["https://openalex.org/I83791580"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5031787246"],"corresponding_institution_ids":["https://openalex.org/I61057504","https://openalex.org/I83791580"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2784},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2784},"fwci":1.5645,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.79689238,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"14","issue":"7","first_page":"1561","last_page":"1561"},"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":0.9998000264167786,"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/T10226","display_name":"Land Use and Ecosystem Services","score":0.9958000183105469,"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/computer-science","display_name":"Computer science","score":0.7130351066589355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6740075349807739},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6673814058303833},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6212366819381714},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5933460593223572},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.5819495916366577},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5321029424667358},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.504761815071106},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47719132900238037},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46174222230911255},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.45767849683761597},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.414419949054718},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.383945107460022},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36350017786026},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2673640847206116},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2388586699962616},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09393951296806335}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7130351066589355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6740075349807739},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6673814058303833},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6212366819381714},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5933460593223572},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.5819495916366577},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5321029424667358},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.504761815071106},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47719132900238037},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46174222230911255},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.45767849683761597},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.414419949054718},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.383945107460022},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36350017786026},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2673640847206116},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2388586699962616},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09393951296806335},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14071561","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14071561","pdf_url":"https://www.mdpi.com/2072-4292/14/7/1561/pdf?version=1648103622","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c2ca9bed32644c8e904f6d1b5bce0f77","is_oa":false,"landing_page_url":"https://doaj.org/article/c2ca9bed32644c8e904f6d1b5bce0f77","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 7, p 1561 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/7/1561/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14071561","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14071561","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14071561","pdf_url":"https://www.mdpi.com/2072-4292/14/7/1561/pdf?version=1648103622","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.75,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G1059679333","display_name":null,"funder_award_id":"2020N5003","funder_id":"https://openalex.org/F4320323190","funder_display_name":"Fujian Provincial Department of Science and Technology"},{"id":"https://openalex.org/G7396444981","display_name":null,"funder_award_id":"2019N5012","funder_id":"https://openalex.org/F4320323190","funder_display_name":"Fujian Provincial Department of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320323190","display_name":"Fujian Provincial Department of Science and Technology","ror":"https://ror.org/00rgzpv08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4220676290.pdf","grobid_xml":"https://content.openalex.org/works/W4220676290.grobid-xml"},"referenced_works_count":69,"referenced_works":["https://openalex.org/W172260869","https://openalex.org/W1584308190","https://openalex.org/W1998979050","https://openalex.org/W2009731464","https://openalex.org/W2098676252","https://openalex.org/W2099540835","https://openalex.org/W2133556703","https://openalex.org/W2144158466","https://openalex.org/W2144313462","https://openalex.org/W2194775991","https://openalex.org/W2253590344","https://openalex.org/W2267317359","https://openalex.org/W2312198368","https://openalex.org/W2397741397","https://openalex.org/W2515306179","https://openalex.org/W2560462200","https://openalex.org/W2579348194","https://openalex.org/W2592962403","https://openalex.org/W2610166850","https://openalex.org/W2752278344","https://openalex.org/W2794055043","https://openalex.org/W2811244448","https://openalex.org/W2884821113","https://openalex.org/W2896206172","https://openalex.org/W2897052332","https://openalex.org/W2921401402","https://openalex.org/W2940726923","https://openalex.org/W2943839452","https://openalex.org/W2945998314","https://openalex.org/W2962766617","https://openalex.org/W2962843773","https://openalex.org/W2963150697","https://openalex.org/W2969634177","https://openalex.org/W2988389512","https://openalex.org/W2990265654","https://openalex.org/W2996445195","https://openalex.org/W2996517671","https://openalex.org/W3001083904","https://openalex.org/W3003387640","https://openalex.org/W3005101149","https://openalex.org/W3013471216","https://openalex.org/W3016355987","https://openalex.org/W3031227608","https://openalex.org/W3036016333","https://openalex.org/W3044895230","https://openalex.org/W3046083520","https://openalex.org/W3047955358","https://openalex.org/W3081291579","https://openalex.org/W3089962227","https://openalex.org/W3092811956","https://openalex.org/W3097361954","https://openalex.org/W3110500136","https://openalex.org/W3112497497","https://openalex.org/W3117373865","https://openalex.org/W3118600741","https://openalex.org/W3121566766","https://openalex.org/W3123352549","https://openalex.org/W3124539583","https://openalex.org/W3145807633","https://openalex.org/W3153652603","https://openalex.org/W3156809691","https://openalex.org/W3164623117","https://openalex.org/W3166623178","https://openalex.org/W3168526407","https://openalex.org/W3175739619","https://openalex.org/W3184401950","https://openalex.org/W3205337742","https://openalex.org/W6606982846","https://openalex.org/W6681561116"],"related_works":["https://openalex.org/W4372048956","https://openalex.org/W4206989953","https://openalex.org/W4283776244","https://openalex.org/W3213976941","https://openalex.org/W4382564271","https://openalex.org/W2604192360","https://openalex.org/W2958985755","https://openalex.org/W2811106690","https://openalex.org/W2889221269","https://openalex.org/W3185156046"],"abstract_inverted_index":{"Monitoring":[0],"and":[1,34,49,78,85,110,116,125,128,141,148,167,180,188,227,266,313],"assessing":[2],"vegetation":[3,31],"using":[4,102],"deep":[5,25],"learning":[6,26],"approaches":[7,27],"has":[8],"shown":[9],"promise":[10],"in":[11,269,323],"forestry":[12],"applications.":[13],"Sample":[14],"labeling":[15,44,73,246,254,272,304,329],"to":[16,67,106,154,163,170,197,209,262,318],"represent":[17],"forest":[18],"complexity":[19],"is":[20,55,315],"the":[21,40,56,69,98,132,159,165,181,211,230,242,249,280,293,308,324,327],"main":[22,99],"limitation":[23],"for":[24,28,123,235,258],"remote":[29],"sensing":[30],"classification":[32],"applications,":[33],"few":[35],"studies":[36],"have":[37],"focused":[38],"on":[39,46,81,248,276],"impact":[41,275],"of":[42,71,113,135,244,310],"sample":[43,72,76,79,161,174,178,183,239,245,253,259,264,271,303,311,322,328],"methods":[45,74,151,247],"model":[47,50,133,199,212,250,277,281,294],"performance":[48,134],"training":[51,124,166,205,213,290],"efficiency.":[52,214],"This":[53,298],"study":[54,299],"first-of-its-kind":[57],"that":[58,218,301],"uses":[59],"Mask":[60,236],"region-based":[61],"convolutional":[62],"neural":[63],"networks":[64],"(Mask":[65],"R-CNN)":[66],"evaluate":[68,198,210],"influence":[70,243],"(including":[75],"size":[77,240,260],"distribution)":[80],"individual":[82],"tree-crown":[83],"detection":[84],"delineation.":[86],"A":[87],"flight":[88],"was":[89,143,207],"conducted":[90],"over":[91],"a":[92,103,111,221,286],"plantation":[93],"with":[94,282,295],"Fokienia":[95],"hodginsii":[96],"as":[97],"tree":[100,119],"species":[101],"Phantom4-Multispectral":[104],"(P4M)":[105],"obtain":[107],"UAV":[108,193],"imagery,":[109],"total":[112],"2061":[114],"manually":[115],"accurately":[117,319],"delineated":[118],"crowns":[120],"were":[121,152,195,233],"used":[122,153,196,234],"validating":[126],"(1689)":[127],"testing":[129],"(372).":[130],"First,":[131],"three":[136],"pre-trained":[137],"backbones":[138],"(ResNet-34,":[139],"ResNet-50,":[140],"ResNet-101)":[142],"evaluated.":[144],"Second,":[145],"random":[146,177,270,296,302],"deleting":[147,150],"clumped":[149,182,263,283],"repeatedly":[155],"delete":[156],"10%":[157],"from":[158,192],"original":[160],"set":[162,179],"reduce":[164,307],"validation":[168],"set,":[169],"simulate":[171],"two":[172],"different":[173],"distributions":[175],"(the":[176],"set).":[184],"Both":[185],"RGB":[186],"image":[187,325],"Multi-band":[189],"images":[190],"derived":[191],"flights":[194],"performance.":[200,251],"Each":[201],"model\u2019s":[202],"average":[203,288],"per-epoch":[204,289],"time":[206,291],"calculated":[208],"The":[215,238],"results":[216],"showed":[217],"ResNet-50":[219],"yielded":[220],"more":[222],"robust":[223],"network":[224],"than":[225,292],"ResNet-34":[226],"ResNet-101":[228],"when":[229],"same":[231],"parameters":[232],"R-CNN.":[237],"determined":[241],"Random":[252],"had":[255,273],"lower":[256],"requirements":[257],"compared":[261],"labeling,":[265],"unlabeled":[267],"trees":[268],"no":[274],"training.":[278],"Additionally,":[279],"samples":[284],"provides":[285],"shorter":[287],"samples.":[297],"demonstrates":[300],"can":[305],"greatly":[306],"requirement":[309],"size,":[312],"it":[314],"not":[316],"necessary":[317],"label":[320],"each":[321],"during":[326],"process.":[330]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}