乐胖代购免代理版

{"id":"https://openalex.org/W3100764680","doi":"https://doi.org/10.1145/3308558.3313394","title":"CBHE: Corner-based Building Height Estimation for Complex Street Scene Images","display_name":"CBHE: Corner-based Building Height Estimation for Complex Street Scene Images","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W3100764680","doi":"https://doi.org/10.1145/3308558.3313394","mag":"3100764680"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3308558.3313394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"proceedings-article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/1904.11128","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029949641","display_name":"Yunxiang Zhao","orcid":"https://orcid.org/0000-0001-6065-811X"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yunxiang Zhao","raw_affiliation_strings":["The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022290876","display_name":"Jianzhong Qi","orcid":"https://orcid.org/0000-0001-6501-9050"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jianzhong Qi","raw_affiliation_strings":["The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100422092","display_name":"Rui Zhang","orcid":"https://orcid.org/0000-0002-8132-6250"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Rui Zhang","raw_affiliation_strings":["The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":17,"citation_normalized_percentile":{"value":0.998075,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":91},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automatic Road Extraction from Remote Sensing Images","score":0.9992,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automatic Road Extraction from Remote Sensing Images","score":0.9992,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Visual Object Tracking and Person Re-identification","score":0.9986,"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"}},{"id":"https://openalex.org/T11164","display_name":"Mapping Forests with Lidar Remote Sensing","score":0.9966,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/tree-height-estimation","display_name":"Tree Height Estimation","score":0.580224},{"id":"https://openalex.org/keywords/motion-detection","display_name":"Motion Detection","score":0.500095},{"id":"https://openalex.org/keywords/pinhole","display_name":"Pinhole (optics)","score":0.4543393}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899521},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.64056885},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6181755},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.50952536},{"id":"https://openalex.org/C2776700484","wikidata":"https://www.wikidata.org/wiki/Q15832623","display_name":"Pinhole (optics)","level":2,"score":0.4543393},{"id":"https://openalex.org/C199996500","wikidata":"https://www.wikidata.org/wiki/Q14369636","display_name":"Pinhole camera model","level":4,"score":0.4156378},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.19150662},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.17360184},{"id":"https://openalex.org/C110898773","wikidata":"https://www.wikidata.org/wiki/Q2933935","display_name":"Camera resectioning","level":2,"score":0.16589943},{"id":"https://openalex.org/C94816000","wikidata":"https://www.wikidata.org/wiki/Q5026006","display_name":"Camera auto-calibration","level":3,"score":0.12724566},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3308558.3313394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1904.11128","pdf_url":"http://arxiv.org/pdf/1904.11128","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://hdl.handle.net/11343/225731","pdf_url":"http://minerva-access.unimelb.edu.au/bitstreams/1acfce97-6492-501e-b81d-a35dad84d331/download","source":{"id":"https://openalex.org/S4377196259","display_name":"Minerva Access (University of Melbourne)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I165779595","host_organization_name":"University of Melbourne","host_organization_lineage":["https://openalex.org/I165779595"],"host_organization_lineage_names":["University of Melbourne"],"type":"repository"},"license":"mit","license_id":"https://openalex.org/licenses/mit","version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1904.11128","pdf_url":"https://arxiv.org/pdf/1904.11128","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.1904.11128","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1904.11128","pdf_url":"http://arxiv.org/pdf/1904.11128","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"score":0.85,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"grants":[],"datasets":[],"versions":["https://openalex.org/W2912942355","https://openalex.org/W3100764680"],"referenced_works_count":47,"referenced_works":["https://openalex.org/W1563625711","https://openalex.org/W1680252513","https://openalex.org/W1898645800","https://openalex.org/W1966728814","https://openalex.org/W1967984006","https://openalex.org/W1978338723","https://openalex.org/W1979633152","https://openalex.org/W1989750313","https://openalex.org/W2000722833","https://openalex.org/W2018459374","https://openalex.org/W2035129548","https://openalex.org/W2085041112","https://openalex.org/W2103054881","https://openalex.org/W2104153972","https://openalex.org/W2104525659","https://openalex.org/W2106077237","https://openalex.org/W2112796928","https://openalex.org/W2129348473","https://openalex.org/W2129587342","https://openalex.org/W2130556178","https://openalex.org/W2132820324","https://openalex.org/W2145618333","https://openalex.org/W2148566104","https://openalex.org/W2153635508","https://openalex.org/W2155893237","https://openalex.org/W2156438113","https://openalex.org/W2167667767","https://openalex.org/W2187089797","https://openalex.org/W2289717345","https://openalex.org/W2406005695","https://openalex.org/W2517180603","https://openalex.org/W2520774990","https://openalex.org/W2526075485","https://openalex.org/W2544192960","https://openalex.org/W2548362856","https://openalex.org/W2563705555","https://openalex.org/W2592281192","https://openalex.org/W2739269730","https://openalex.org/W2762262611","https://openalex.org/W2780675120","https://openalex.org/W2792096654","https://openalex.org/W2794566392","https://openalex.org/W2903396356","https://openalex.org/W2963149653","https://openalex.org/W3099206234","https://openalex.org/W4301045096","https://openalex.org/W567217126"],"related_works":["https://openalex.org/W3187327914","https://openalex.org/W3116076068","https://openalex.org/W2775347418","https://openalex.org/W2772917594","https://openalex.org/W2755342338","https://openalex.org/W2533654844","https://openalex.org/W2229312674","https://openalex.org/W2166024367","https://openalex.org/W2058170566","https://openalex.org/W2054266717"],"abstract_inverted_index":{"Building":[0],"height":[1,21,67,105,166,208],"estimation":[2,22,106,209],"is":[3,35],"important":[4],"in":[5,80,86,122,206],"many":[6],"applications":[7],"such":[8],"as":[9],"3D":[10],"city":[11],"reconstruction,":[12],"urban":[13],"planning,":[14],"and":[15,28,63,93,112,119,133,148,151,159,186],"navigation.":[16],"Recently,":[17],"a":[18,103,140,180],"new":[19],"building":[20,61,66,104,110,117,128,165,184,207],"method":[23,34,57,77],"using":[24],"street":[25,83,123],"scene":[26,84,124],"images":[27,85,125],"2D":[29,131],"maps":[30,132],"was":[31],"proposed.":[32],"This":[33],"more":[36],"scalable":[37],"than":[38],"traditional":[39],"methods":[40],"that":[41,75,175],"use":[42,139],"high-resolution":[43],"optical":[44],"data,":[45,47],"LiDAR":[46],"or":[48],"RADAR":[49],"data":[50],"which":[51,87],"are":[52,96],"expensive":[53],"to":[54,59,98,146],"obtain.":[55],"The":[56],"needs":[58],"detect":[60],"rooflines":[62,95,160],"then":[64],"compute":[65],"via":[68,167],"the":[69,94,134,156,168,176,192,200],"pinhole":[70,169],"camera":[71,135,170],"model.":[72,171],"We":[73,100],"observe":[74],"this":[76],"has":[78],"limitations":[79],"handling":[81],"complex":[82],"buildings":[88],"overlap":[89],"with":[90,191],"each":[91],"other":[92],"difficult":[97],"locate.":[99],"propose":[101],"CBHE,":[102],"algorithm":[107,202],"considering":[108],"both":[109],"corners":[111,158],"rooflines.":[113],"CBHE":[114,163,198],"first":[115],"obtains":[116],"corner":[118,150,185],"roofline":[120,152,187],"candidates":[121],"based":[126],"on":[127,155,183],"footprints":[129],"from":[130,161],"parameters.":[136],"Then,":[137],"we":[138],"deep":[141],"neural":[142],"network":[143],"named":[144],"BuildingNet":[145,178],"classify":[147],"filter":[149],"candidates.":[153],"Based":[154],"valid":[157],"BuildingNet,":[162],"computes":[164],"Experimental":[172],"results":[173],"show":[174],"proposed":[177],"yields":[179],"higher":[181],"accuracy":[182],"candidate":[188],"filtering":[189],"compared":[190],"state-of-the-art":[193],"open":[194],"set":[195],"classifiers.":[196],"Meanwhile,":[197],"outperforms":[199],"baseline":[201],"by":[203],"over":[204],"10%":[205],"accuracy.":[210]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3100764680","counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2024-11-17T23:04:51.799853","created_date":"2020-11-23"}