{"id":"https://openalex.org/W4391547565","doi":"https://doi.org/10.1109/tgrs.2024.3362601","title":"HigherNet-DST: Higher-Resolution Network With Dynamic Scale Training for Rooftop Delineation","display_name":"HigherNet-DST: Higher-Resolution Network With Dynamic Scale Training for Rooftop Delineation","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391547565","doi":"https://doi.org/10.1109/tgrs.2024.3362601"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3362601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3362601","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5049403375","display_name":"Hongjie He","orcid":"https://orcid.org/0000-0003-3839-5821"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Hongjie He","raw_affiliation_strings":["Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032270954","display_name":"Lingfei Ma","orcid":"https://orcid.org/0000-0001-8893-9693"},"institutions":[{"id":"https://openalex.org/I137867983","display_name":"Central University of Finance and Economics","ror":"https://ror.org/008e3hf02","country_code":"CN","type":"education","lineage":["https://openalex.org/I137867983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingfei Ma","raw_affiliation_strings":["School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China","institution_ids":["https://openalex.org/I137867983"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100613889","display_name":"Jonathan Li","orcid":"https://orcid.org/0000-0001-7899-0049"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jonathan Li","raw_affiliation_strings":["Department of Geography and Environmental Management and the Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada","Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Management and the Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5049403375"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":2.1325,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.84695595,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9988999962806702,"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":"Automated Road and Building Extraction","score":0.9988999962806702,"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":"Video Surveillance and Tracking Methods","score":0.9979000091552734,"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":"Remote Sensing and LiDAR Applications","score":0.996999979019165,"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/computer-science","display_name":"Computer science","score":0.7500976920127869},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6596860885620117},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.6031367778778076},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47359779477119446},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4617573916912079},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4273764193058014},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4197778105735779},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41486552357673645},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33083024621009827},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.20657658576965332},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12368497252464294},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10194084048271179}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7500976920127869},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6596860885620117},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6031367778778076},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47359779477119446},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4617573916912079},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4273764193058014},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4197778105735779},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41486552357673645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33083024621009827},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.20657658576965332},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12368497252464294},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10194084048271179},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"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":1,"locations":[{"id":"doi:10.1109/tgrs.2024.3362601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3362601","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/13"},{"display_name":"Sustainable cities and communities","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1614057970","display_name":null,"funder_award_id":"42101451","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1715742766","display_name":null,"funder_award_id":"201906180088","funder_id":"https://openalex.org/F4320335579","funder_display_name":"University Postgraduate Programme"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321599","display_name":"Central University of Finance and Economics","ror":"https://ror.org/008e3hf02"},{"id":"https://openalex.org/F4320335579","display_name":"University Postgraduate Programme","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2065972554","https://openalex.org/W2074112114","https://openalex.org/W2104095591","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2601564443","https://openalex.org/W2609402060","https://openalex.org/W2623331213","https://openalex.org/W2755226765","https://openalex.org/W2768489488","https://openalex.org/W2897593716","https://openalex.org/W2908320224","https://openalex.org/W2916798096","https://openalex.org/W2924260171","https://openalex.org/W2949763629","https://openalex.org/W2953809838","https://openalex.org/W2963150697","https://openalex.org/W2963406768","https://openalex.org/W2963857746","https://openalex.org/W2981989409","https://openalex.org/W2982206001","https://openalex.org/W2988452521","https://openalex.org/W2994434065","https://openalex.org/W3018757597","https://openalex.org/W3019847943","https://openalex.org/W3034399482","https://openalex.org/W3088431851","https://openalex.org/W3094730317","https://openalex.org/W3104035745","https://openalex.org/W3110187642","https://openalex.org/W3137744231","https://openalex.org/W3138977901","https://openalex.org/W3163631062","https://openalex.org/W3166457133","https://openalex.org/W3170019900","https://openalex.org/W3171450708","https://openalex.org/W3176363936","https://openalex.org/W3200384228","https://openalex.org/W3201387179","https://openalex.org/W3217005392","https://openalex.org/W3217355560","https://openalex.org/W4210874378","https://openalex.org/W4225134630","https://openalex.org/W4281481157","https://openalex.org/W4283383339","https://openalex.org/W4297824736","https://openalex.org/W4319599426","https://openalex.org/W4361195764","https://openalex.org/W6684191040","https://openalex.org/W6751325469","https://openalex.org/W6770510780","https://openalex.org/W6792039502","https://openalex.org/W6922074832"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W3147584709","https://openalex.org/W2977677679","https://openalex.org/W1992327129","https://openalex.org/W2381986121","https://openalex.org/W2370918718","https://openalex.org/W4224009465","https://openalex.org/W2000663367","https://openalex.org/W2118155885","https://openalex.org/W2163515958"],"abstract_inverted_index":{"High-definition":[0],"(HD)":[1],"maps":[2,21],"of":[3,18,67,112,178,184,194,233],"building":[4,152,237],"rooftops":[5],"or":[6],"footprints":[7],"are":[8,136],"important":[9],"for":[10,236],"urban":[11],"application":[12],"and":[13,32,42,154,191,213],"disaster":[14],"management.":[15],"Rapid":[16],"creation":[17],"such":[19],"HD":[20],"through":[22],"rooftop":[23,52,65,95,166],"delineation":[24,53,66,142,167],"at":[25],"the":[26,47,55,79,90,98,103,109,120,128,132,141,155,162,169,175,187,197,209,214,222,226,230],"city":[27],"scale":[28,48,91,113],"using":[29],"high-resolution":[30,133],"satellite":[31],"aerial":[33],"images":[34],"with":[35,83,181],"deep":[36],"learning":[37],"methods":[38,59],"has":[39],"become":[40],"feasible":[41],"drawn":[43],"much":[44],"attention.":[45],"However,":[46],"variance":[49,92],"issue":[50],"in":[51,64,94,102,165],"limited":[54],"overall":[56],"performance.":[57,143],"Existing":[58],"exhibit":[60],"considerably":[61],"poor":[62],"performance":[63,164,177,224,232],"small":[68],"buildings.":[69],"In":[70],"this":[71],"paper,":[72],"we":[73],"propose":[74],"a":[75,116],"new":[76],"method,":[77],"namely":[78,119],"Higher":[80,121],"Resolution":[81,122],"Network":[82],"Dynamic":[84],"Scale":[85],"Training":[86],"(HigherNet-DST)":[87],"to":[88,107,126,138],"overcome":[89],"problem":[93],"delineation.":[96,239],"Specifically,":[97],"DST":[99],"is":[100,124],"applied":[101],"model":[104],"training":[105],"phase":[106],"reduce":[108],"negative":[110],"impact":[111],"variance.":[114],"Then,":[115],"scale-aware":[117],"backbone,":[118],"Network,":[123],"adopted":[125],"enhance":[127],"feature":[129],"representation.":[130],"Finally,":[131],"supervision":[134],"targets":[135],"used":[137],"further":[139],"boost":[140],"Our":[144],"method":[145,160,180,219,235],"was":[146],"tested":[147],"on":[148,186],"four":[149],"publicly":[150],"accessible":[151],"datasets":[153],"results":[156],"demonstrated":[157],"that":[158],"our":[159,179,218,234],"achieved":[161,221],"highest":[163,223],"among":[168,225],"existing":[170],"methods.":[171,207],"Extensive":[172],"experiments":[173],"showed":[174],"superior":[176],"an":[182,192],"AP":[183],"68.5%":[185],"AICrowd":[188],"Building":[189,199,211,216],"Dataset":[190,212],"IoU":[193],"82.6%":[195],"On":[196,208],"Inria":[198],"Dataset,":[200,217],"respectively,":[201],"which":[202],"surpassed":[203],"many":[204],"state-of-the-art":[205],"(SOTA)":[206],"WHU":[210],"Waterloo":[215],"also":[220],"benchmarked":[227],"methods,":[228],"showing":[229],"high":[231],"boundary":[238]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
