{"id":"https://openalex.org/W2289335921","doi":"https://doi.org/10.1155/2016/1795205","title":"Individual Building Rooftop and Tree Crown Segmentation from High-Resolution Urban Aerial Optical Images","display_name":"Individual Building Rooftop and Tree Crown Segmentation from High-Resolution Urban Aerial Optical Images","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2289335921","doi":"https://doi.org/10.1155/2016/1795205","mag":"2289335921"},"language":"en","primary_location":{"id":"doi:10.1155/2016/1795205","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2016/1795205","pdf_url":"http://downloads.hindawi.com/journals/js/2016/1795205.pdf","source":{"id":"https://openalex.org/S96783963","display_name":"Journal of Sensors","issn_l":"1687-725X","issn":["1687-725X","1687-7268"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"http://downloads.hindawi.com/journals/js/2016/1795205.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054785818","display_name":"Jichao Jiao","orcid":"https://orcid.org/0000-0002-1200-5525"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jichao Jiao","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"],"raw_orcid":"https://orcid.org/0000-0002-1200-5525","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102222671","display_name":"Zhongliang Deng","orcid":"https://orcid.org/0000-0002-1984-8613"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongliang Deng","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5054785818"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":{"value":2375,"currency":"USD","value_usd":2375},"apc_paid":{"value":2375,"currency":"USD","value_usd":2375},"fwci":0.3486,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.61062682,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2016","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9987000226974487,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7307892441749573},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6990301012992859},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6404990553855896},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5466443300247192},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5422691702842712},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5162755846977234},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4928152859210968},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4565418064594269},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.4313027560710907},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.4192132353782654},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39110180735588074},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3770502209663391},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23243707418441772},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.13991934061050415}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7307892441749573},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6990301012992859},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6404990553855896},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5466443300247192},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5422691702842712},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5162755846977234},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4928152859210968},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4565418064594269},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4313027560710907},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.4192132353782654},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39110180735588074},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3770502209663391},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23243707418441772},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.13991934061050415},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1155/2016/1795205","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2016/1795205","pdf_url":"http://downloads.hindawi.com/journals/js/2016/1795205.pdf","source":{"id":"https://openalex.org/S96783963","display_name":"Journal of Sensors","issn_l":"1687-725X","issn":["1687-725X","1687-7268"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Sensors","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ed36d4a0f0a2437388e8fb67cc7a3639","is_oa":true,"landing_page_url":"https://doaj.org/article/ed36d4a0f0a2437388e8fb67cc7a3639","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Sensors, Vol 2016 (2016)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2016/1795205","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2016/1795205","pdf_url":"http://downloads.hindawi.com/journals/js/2016/1795205.pdf","source":{"id":"https://openalex.org/S96783963","display_name":"Journal of Sensors","issn_l":"1687-725X","issn":["1687-725X","1687-7268"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8500000238418579,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G5249506375","display_name":null,"funder_award_id":"2014AA123103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7633787162","display_name":null,"funder_award_id":"61401040","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8477631735","display_name":null,"funder_award_id":"2015AA124103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2289335921.pdf","grobid_xml":"https://content.openalex.org/works/W2289335921.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W56937753","https://openalex.org/W1544008493","https://openalex.org/W1548953334","https://openalex.org/W1968180959","https://openalex.org/W1983935692","https://openalex.org/W2052527750","https://openalex.org/W2078558728","https://openalex.org/W2080080852","https://openalex.org/W2080157231","https://openalex.org/W2082801088","https://openalex.org/W2093186893","https://openalex.org/W2098083083","https://openalex.org/W2099637316","https://openalex.org/W2119531662","https://openalex.org/W2128272608","https://openalex.org/W2132947399","https://openalex.org/W2133990480","https://openalex.org/W2145023731","https://openalex.org/W2153384129","https://openalex.org/W2155910279","https://openalex.org/W2162655965","https://openalex.org/W2170282673"],"related_works":["https://openalex.org/W1989735375","https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W2047973478","https://openalex.org/W2067569035","https://openalex.org/W2090985514","https://openalex.org/W2394466068","https://openalex.org/W1987683558","https://openalex.org/W2034727732","https://openalex.org/W2127305659"],"abstract_inverted_index":{"We":[0],"segment":[1,168],"buildings":[2,42,171],"and":[3,11,45,64,106,127,146,170,176,180],"trees":[4,47,169],"from":[5,43,48,58,69],"aerial":[6],"photographs":[7],"by":[8,17,110],"using":[9,18,124],"superpixels,":[10],"we":[12],"estimate":[13],"the":[14,50,78,112,121,128,131,147,157,164,181,189],"tree\u2019s":[15],"parameters":[16,149,183],"a":[19,101],"cost":[20,113],"function":[21],"proposed":[22,33,165],"in":[23,151,185],"this":[24,152],"paper.":[25],"A":[26],"method":[27,166],"based":[28,80],"on":[29,81],"image":[30,132],"complexity":[31],"is":[32,88,117,138],"to":[34,40,76,90,119,156],"refine":[35],"superpixels":[36,93],"boundaries.":[37],"In":[38],"order":[39],"classify":[41,46,91],"ground":[44,158,190],"grass,":[49],"salient":[51],"feature":[52],"vectors":[53,73],"that":[54,163],"include":[55],"colors,":[56],"Features":[57],"Accelerated":[59],"Segment":[60],"Test":[61],"(FAST)":[62],"corners,":[63],"Gabor":[65],"edges":[66],"are":[67,74,108,154,184],"extracted":[68],"refined":[70,92],"superpixels.":[71],"The":[72,85,98,115],"used":[75,89,118],"train":[77],"classifier":[79,87],"Naive":[82],"Bayes":[83],"classifier.":[84],"trained":[86],"as":[94],"object":[95],"or":[96],"nonobject.":[97],"properties":[99],"of":[100],"tree,":[102],"including":[103],"its":[104],"locations":[105],"radius,":[107],"estimated":[109],"minimizing":[111],"function.":[114],"shadow":[116],"calculate":[120],"tree":[122,148,182],"height":[123],"sun":[125],"angle":[126],"time":[129],"when":[130],"was":[133],"taken.":[134],"Our":[135],"segmentation":[136,144],"algorithm":[137],"compared":[139,155],"with":[140,188],"other":[141],"two":[142],"state-of-the-art":[143],"algorithms,":[145],"obtained":[150],"paper":[153],"truth":[159,191],"data.":[160,192],"Experiments":[161],"show":[162],"can":[167],"appropriately,":[172],"yielding":[173],"higher":[174],"precision":[175],"better":[177],"recall":[178],"rates,":[179],"good":[186],"agreement":[187]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
