{"id":"https://openalex.org/W4402261331","doi":"https://doi.org/10.1109/igarss53475.2024.10642737","title":"Individual Tree Crown Segmentation in Subtropical Broadleaf Forests Using UAV-based Ultrahigh-Resolution RGB Data","display_name":"Individual Tree Crown Segmentation in Subtropical Broadleaf Forests Using UAV-based Ultrahigh-Resolution RGB Data","publication_year":2024,"publication_date":"2024-07-07","ids":{"openalex":"https://openalex.org/W4402261331","doi":"https://doi.org/10.1109/igarss53475.2024.10642737"},"language":"en","primary_location":{"id":"doi:10.1109/igarss53475.2024.10642737","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10642737","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","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/A5082458031","display_name":"Ruoning Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153035","display_name":"Shanghai Construction Group (China)","ror":"https://ror.org/04azzhq34","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210153035"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruoning Zhu","raw_affiliation_strings":["The Third Construction CO., LTD of China Construction First Group,Beijing,China,100161"],"affiliations":[{"raw_affiliation_string":"The Third Construction CO., LTD of China Construction First Group,Beijing,China,100161","institution_ids":["https://openalex.org/I4210153035"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000870454","display_name":"Guoqi Chai","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqi Chai","raw_affiliation_strings":["Beijing Forestry University,College of Forestry,Beijing,China,100083"],"affiliations":[{"raw_affiliation_string":"Beijing Forestry University,College of Forestry,Beijing,China,100083","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031348935","display_name":"Xin Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210114891","display_name":"Institute of Forest Resource Information Techniques","ror":"https://ror.org/01h5d6x15","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114891","https://openalex.org/I4210128615","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Tian","raw_affiliation_strings":["Institute of Forest Resource Information Techniques Chinese Academy of Forestry,Beijing,China,100091"],"affiliations":[{"raw_affiliation_string":"Institute of Forest Resource Information Techniques Chinese Academy of Forestry,Beijing,China,100091","institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082458031"],"corresponding_institution_ids":["https://openalex.org/I4210153035"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13266495,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"31","issue":null,"first_page":"3097","last_page":"3099"},"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":0.998199999332428,"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/T11880","display_name":"Forest ecology and management","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2309","display_name":"Nature and Landscape Conservation"},"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":0.6411887407302856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5653669238090515},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5593621730804443},{"id":"https://openalex.org/keywords/crown","display_name":"Crown (dentistry)","score":0.5593433976173401},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5295052528381348},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5170174837112427},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.49975013732910156},{"id":"https://openalex.org/keywords/subtropics","display_name":"Subtropics","score":0.4783448874950409},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44975653290748596},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4069575071334839},{"id":"https://openalex.org/keywords/forestry","display_name":"Forestry","score":0.32185786962509155},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.27052032947540283},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14965522289276123},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.11189877986907959},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.058100759983062744}],"concepts":[{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.6411887407302856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5653669238090515},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5593621730804443},{"id":"https://openalex.org/C2778400979","wikidata":"https://www.wikidata.org/wiki/Q143720","display_name":"Crown (dentistry)","level":2,"score":0.5593433976173401},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5295052528381348},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5170174837112427},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.49975013732910156},{"id":"https://openalex.org/C14168384","wikidata":"https://www.wikidata.org/wiki/Q16305538","display_name":"Subtropics","level":2,"score":0.4783448874950409},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44975653290748596},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4069575071334839},{"id":"https://openalex.org/C97137747","wikidata":"https://www.wikidata.org/wiki/Q38112","display_name":"Forestry","level":1,"score":0.32185786962509155},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.27052032947540283},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14965522289276123},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.11189877986907959},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.058100759983062744},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","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},{"id":"https://openalex.org/C199343813","wikidata":"https://www.wikidata.org/wiki/Q12128","display_name":"Dentistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss53475.2024.10642737","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10642737","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.4399999976158142,"display_name":"Life in Land"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323156","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2121947440","https://openalex.org/W2804625175","https://openalex.org/W4206999712","https://openalex.org/W4283829529","https://openalex.org/W4365812616"],"related_works":["https://openalex.org/W2590418898","https://openalex.org/W2026089796","https://openalex.org/W4211144375","https://openalex.org/W1966682116","https://openalex.org/W4396603047","https://openalex.org/W1998232909","https://openalex.org/W2367081626","https://openalex.org/W61034052","https://openalex.org/W2315757411","https://openalex.org/W1556261626"],"abstract_inverted_index":{"Rapid":[0],"and":[1,15,32,66,114,118],"accurate":[2],"extraction":[3,125],"of":[4,18,42,74,81,111],"individual":[5],"tree":[6,30],"crown":[7,27,86,123],"information":[8,124],"is":[9],"the":[10,26,33,64,71,109],"basis":[11],"for":[12,84,122],"fine":[13],"investigation":[14],"scientific":[16],"management":[17],"forest":[19,94,128],"resources.":[20],"However,":[21],"in":[22,90,95,126],"subtropical":[23,92],"broad-leaved":[24],"forests,":[25],"contains":[28],"multiple":[29],"apexes,":[31],"commonly":[34],"used":[35],"watershed":[36,54,112],"segmentation":[37,51,55,87,113],"algorithms":[38],"have":[39],"different":[40],"degrees":[41],"over-segmentation.":[43],"In":[44],"this":[45],"study,":[46],"we":[47],"propose":[48],"an":[49,116],"individual-tree":[50,85],"method":[52,83,106,121],"with":[53,57],"combined":[56],"spectral-texture-intensity-controlled":[58],"normalized":[59],"cutting,":[60],"which":[61],"directly":[62],"utilizes":[63],"DSM":[65],"DOM":[67],"data":[68],"generated":[69],"from":[70],"high-resolution":[72],"images":[73],"unmanned":[75],"aerial":[76],"vehicles":[77],"(UAVs).":[78],"The":[79,101],"effectiveness":[80],"our":[82,105],"was":[88],"verified":[89],"a":[91],"broadleaf":[93],"southern":[96],"China":[97],"(F=0.87,":[98],"R=0.93,":[99],"P=0.82).":[100],"results":[102],"show":[103],"that":[104],"can":[107],"reduce":[108],"over-segmentation":[110],"provide":[115],"efficient":[117],"low-cost":[119],"new":[120],"complex":[127],"environments.":[129]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
