{"id":"https://openalex.org/W4306166990","doi":"https://doi.org/10.5194/isprs-annals-x-4-w3-2022-173-2022","title":"BUILDING FOOTPRINT SEGMENTATION USING TRANSFER LEARNING: A CASE STUDY OF THE CITY OF MELBOURNE","display_name":"BUILDING FOOTPRINT SEGMENTATION USING TRANSFER LEARNING: A CASE STUDY OF THE CITY OF MELBOURNE","publication_year":2022,"publication_date":"2022-10-14","ids":{"openalex":"https://openalex.org/W4306166990","doi":"https://doi.org/10.5194/isprs-annals-x-4-w3-2022-173-2022"},"language":"en","primary_location":{"id":"doi:10.5194/isprs-annals-x-4-w3-2022-173-2022","doi":"https://doi.org/10.5194/isprs-annals-x-4-w3-2022-173-2022","is_oa":true,"landing_page_url":"https://doi.org/10.5194/isprs-annals-x-4-w3-2022-173-2022","pdf_url":"https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W3-2022/173/2022/isprs-annals-X-4-W3-2022-173-2022.pdf","source":{"id":"https://openalex.org/S2737735205","display_name":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","issn_l":"2194-9042","issn":["2194-9042","2194-9050","2196-6346"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","oarl","vabb-shw"],"host_organization":"https://openalex.org/P4310313756","host_organization_name":"Copernicus Publications","host_organization_lineage":["https://openalex.org/P4310313756"],"host_organization_lineage_names":["Copernicus Publications"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W3-2022/173/2022/isprs-annals-X-4-W3-2022-173-2022.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007445923","display_name":"Bipul Neupane","orcid":"https://orcid.org/0000-0002-5331-9897","observed_orcids":["https://orcid.org/0000-0002-5331-9897"]},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"B. Neupane","raw_affiliation_strings":["Department of Infrastructure Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne VIC 3010, Australia"],"raw_orcid":"https://orcid.org/0000-0002-5331-9897","affiliations":[{"raw_affiliation_string":"Department of Infrastructure Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne VIC 3010, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084255329","display_name":"Jagannath Aryal","orcid":"https://orcid.org/0000-0002-4875-2127","observed_orcids":["https://orcid.org/0000-0002-4875-2127"]},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The 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":"J. Aryal","raw_affiliation_strings":["Department of Infrastructure Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne VIC 3010, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Infrastructure Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne VIC 3010, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024890229","display_name":"A. Rajabifard","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The 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":"A. Rajabifard","raw_affiliation_strings":["Department of Infrastructure Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne VIC 3010, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Infrastructure Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne VIC 3010, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5007445923"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":0.2774,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.42400865,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"X-4/W3-2022","issue":null,"first_page":"173","last_page":"179"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9980000257492065,"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"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9980000257492065,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0007999999797903001,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.000699999975040555,"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/transfer-learning","display_name":"transfer learning","score":0.8519999980926514},{"id":"https://openalex.org/keywords/u-net","display_name":"U-Net","score":0.699999988079071},{"id":"https://openalex.org/keywords/very-high-resolution-imagery","display_name":"very high resolution imagery","score":0.8510000109672546},{"id":"https://openalex.org/keywords/uav-imagery","display_name":"UAV imagery","score":0.7549999952316284},{"id":"https://openalex.org/keywords/domain-shift","display_name":"domain shift","score":0.7490000128746033},{"id":"https://openalex.org/keywords/small-object-detection","display_name":"small object detection","score":0.5600000023841858},{"id":"https://openalex.org/keywords/contextual-features","display_name":"contextual features","score":0.574999988079071},{"id":"https://openalex.org/keywords/training-set-size","display_name":"training data size","score":0.492000013589859},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.7710000276565552},{"id":"https://openalex.org/keywords/learning-rate-optimization","display_name":"learning rate optimization","score":0.7210000157356262},{"id":"https://openalex.org/keywords/city-of-melbourne","display_name":"City of Melbourne","score":0.5830000042915344},{"id":"https://openalex.org/keywords/building-extraction","display_name":"building extraction","score":0.4189999997615814}],"concepts":[{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.8005170822143555},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7538689374923706},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7110248804092407},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6944297552108765},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6742628812789917},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6632049083709717},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6548311114311218},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4599728584289551},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4591052532196045},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4371328055858612},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4343448579311371},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3869263231754303},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3735274076461792},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11701765656471252},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.09988054633140564},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5194/isprs-annals-x-4-w3-2022-173-2022","doi":"https://doi.org/10.5194/isprs-annals-x-4-w3-2022-173-2022","is_oa":true,"landing_page_url":"https://doi.org/10.5194/isprs-annals-x-4-w3-2022-173-2022","pdf_url":"https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W3-2022/173/2022/isprs-annals-X-4-W3-2022-173-2022.pdf","source":{"id":"https://openalex.org/S2737735205","display_name":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","issn_l":"2194-9042","issn":["2194-9042","2194-9050","2196-6346"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","oarl","vabb-shw"],"host_organization":"https://openalex.org/P4310313756","host_organization_name":"Copernicus Publications","host_organization_lineage":["https://openalex.org/P4310313756"],"host_organization_lineage_names":["Copernicus Publications"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ecefa9471c8143d4a887666e1f262f11","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/ecefa9471c8143d4a887666e1f262f11","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,"listed_in":[],"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":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-4-W3-2022, Pp 173-179 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.5194/isprs-annals-x-4-w3-2022-173-2022","doi":"https://doi.org/10.5194/isprs-annals-x-4-w3-2022-173-2022","is_oa":true,"landing_page_url":"https://doi.org/10.5194/isprs-annals-x-4-w3-2022-173-2022","pdf_url":"https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W3-2022/173/2022/isprs-annals-X-4-W3-2022-173-2022.pdf","source":{"id":"https://openalex.org/S2737735205","display_name":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","issn_l":"2194-9042","issn":["2194-9042","2194-9050","2196-6346"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","oarl","vabb-shw"],"host_organization":"https://openalex.org/P4310313756","host_organization_name":"Copernicus Publications","host_organization_lineage":["https://openalex.org/P4310313756"],"host_organization_lineage_names":["Copernicus Publications"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.875,"display_name":"Sustainable cities and communities"},{"id":"https://metadata.un.org/sdg/9","score":0.428600013256073,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320974","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4306166990.pdf","grobid_xml":"https://content.openalex.org/works/W4306166990.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2031489346","https://openalex.org/W2108598243","https://openalex.org/W2165698076","https://openalex.org/W2395579298","https://openalex.org/W2887280559","https://openalex.org/W2897936062","https://openalex.org/W2908320224","https://openalex.org/W2915971115","https://openalex.org/W3092534080","https://openalex.org/W3122507398","https://openalex.org/W3129544296","https://openalex.org/W4239072543","https://openalex.org/W4255421341"],"related_works":["https://openalex.org/W7204224194","https://openalex.org/W3206422633","https://openalex.org/W7160449166","https://openalex.org/W4281674581","https://openalex.org/W4385261374","https://openalex.org/W7140073892","https://openalex.org/W7076435913","https://openalex.org/W4280522787","https://openalex.org/W3184607044","https://openalex.org/W6977369805"],"abstract_inverted_index":{"Abstract.":[0],"Earth":[1],"observation":[2],"data":[3],"including":[4],"very":[5],"high-resolution":[6],"(VHR)":[7],"imagery":[8],"from":[9,52,132,163],"satellites":[10],"and":[11,26,43,70,87,112,146,153,180,182,214],"unmanned":[12],"aerial":[13],"vehicles":[14],"(UAVs)":[15],"are":[16,37],"the":[17,30,41,105,120,128,140,143,164,170,177,183,197,202,206,222],"primary":[18],"sources":[19],"for":[20,110],"highly":[21,174],"accurate":[22],"building":[23],"footprint":[24],"segmentation":[25],"extraction.":[27],"However,":[28],"with":[29,149],"increase":[31,104],"in":[32,40],"spatial":[33],"resolution,":[34],"smaller":[35,125],"objects":[36],"prominently":[38],"visible":[39],"images,":[42],"using":[44,64],"intelligent":[45],"approaches":[46],"like":[47],"deep":[48],"learning":[49,117,155],"(DL)":[50],"suffers":[51],"several":[53],"problems.":[54],"In":[55,91],"this":[56],"paper,":[57],"we":[58,94,114,138,185],"outline":[59],"four":[60],"prominent":[61],"problems":[62],"while":[63,220],"DL-based":[65],"methods":[66],"(P1,":[67],"P2,":[68],"P3,":[69,113],"P4):":[71],"(P1)":[72],"lack":[73],"of":[74,79,166,200,208,224],"contextual":[75,106],"features,":[76],"(P2)":[77],"requirement":[78],"a":[80,96,124,133,160],"large":[81],"training":[82,151,209,215],"dataset,":[83],"(P3)":[84],"domain-shift":[85],"problem,":[86],"(P4)":[88],"computational":[89,147],"expense.":[90],"tackling":[92],"P1,":[93],"modify":[95],"commonly":[97],"used":[98],"DL":[99,121],"architecture":[100],"called":[101],"U-Net":[102,172,179,204],"to":[103,118],"feature":[107],"information.":[108],"Likewise,":[109],"P2":[111],"use":[115],"transfer":[116],"fine-tune":[119],"model":[122],"on":[123,159],"dataset":[126,184],"utilising":[127],"knowledge":[129],"previously":[130],"gained":[131],"larger":[134],"dataset.":[135,195],"For":[136],"P4,":[137],"study":[139,162],"trade-off":[141],"between":[142],"network\u2019s":[144],"performance":[145],"expense":[148],"reduced":[150],"parameters":[152,210],"optimum":[154],"rates.":[156],"Our":[157],"experiments":[158],"case":[161],"City":[165],"Melbourne":[167],"show":[168],"that":[169],"modified":[171,203],"is":[173,187],"robust":[175,190],"than":[176,191],"original":[178],"SegNet,":[181],"develop":[186],"significantly":[188],"more":[189],"an":[192],"existing":[193],"benchmark":[194],"Furthermore,":[196],"overall":[198],"method":[199],"fine-tuning":[201],"reduces":[205],"number":[207],"by":[211,217],"300":[212],"times":[213,219],"time":[216],"2.5":[218],"preserving":[221],"precision":[223],"segmentation.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2022-10-14T00:00:00"}