{"id":"https://openalex.org/W4322728013","doi":"https://doi.org/10.1109/lgrs.2023.3250091","title":"ConvBNet: A Convolutional Network for Building Footprint Extraction","display_name":"ConvBNet: A Convolutional Network for Building Footprint Extraction","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4322728013","doi":"https://doi.org/10.1109/lgrs.2023.3250091"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2023.3250091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3250091","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5100707699","display_name":"Tong Yu","orcid":"https://orcid.org/0000-0002-4268-4258"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tong Yu","raw_affiliation_strings":["Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China"],"raw_orcid":"https://orcid.org/0000-0002-4268-4258","affiliations":[{"raw_affiliation_string":"Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074647574","display_name":"Panpan Tang","orcid":"https://orcid.org/0000-0001-7464-1968"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Panpan Tang","raw_affiliation_strings":["Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China"],"raw_orcid":"https://orcid.org/0000-0001-7464-1968","affiliations":[{"raw_affiliation_string":"Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103114339","display_name":"Bo Zhao","orcid":"https://orcid.org/0000-0002-2120-2571"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo Zhao","raw_affiliation_strings":["Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101562164","display_name":"Shi Bai","orcid":"https://orcid.org/0000-0002-8894-7025"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi Bai","raw_affiliation_strings":["Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040814281","display_name":"Peng Gou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng Gou","raw_affiliation_strings":["Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048397143","display_name":"Jiachun Liao","orcid":"https://orcid.org/0000-0001-7439-4645"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiachun Liao","raw_affiliation_strings":["Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Technology Research Center, Nanhu Laboratory, Jiaxing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060423849","display_name":"Caifeng Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092870","display_name":"Jiaxing University","ror":"https://ror.org/00j2a7k55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210092870"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caifeng Jin","raw_affiliation_strings":["School of Civil Engineering and Architecture, Jiaxing Nanhu University, Jiaxing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Civil Engineering and Architecture, Jiaxing Nanhu University, Jiaxing, China","institution_ids":["https://openalex.org/I4210092870"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4282,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.87603533,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9995999932289124,"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":0.9995999932289124,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9937000274658203,"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/footprint","display_name":"Footprint","score":0.841658353805542},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7810282111167908},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.673371434211731},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6709522008895874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5437068939208984},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4919402301311493},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4820864796638489},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.4457448720932007},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.43535545468330383},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.4344959855079651},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40669238567352295},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36848539113998413},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3383704423904419},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.30687785148620605},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07889577746391296},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0726773738861084}],"concepts":[{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.841658353805542},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7810282111167908},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.673371434211731},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6709522008895874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5437068939208984},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4919402301311493},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4820864796638489},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.4457448720932007},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.43535545468330383},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.4344959855079651},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40669238567352295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36848539113998413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3383704423904419},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.30687785148620605},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07889577746391296},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0726773738861084},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2023.3250091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3250091","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2340393436","display_name":null,"funder_award_id":"41901360","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2080518886","https://openalex.org/W2085665642","https://openalex.org/W2134337515","https://openalex.org/W2140979518","https://openalex.org/W2141573384","https://openalex.org/W2412782625","https://openalex.org/W2531409750","https://openalex.org/W2592939477","https://openalex.org/W2787091153","https://openalex.org/W2787614951","https://openalex.org/W2802254833","https://openalex.org/W2908320224","https://openalex.org/W2916798096","https://openalex.org/W3053564872","https://openalex.org/W3122259118","https://openalex.org/W3126435384","https://openalex.org/W3138516171","https://openalex.org/W3157124226","https://openalex.org/W3177052299","https://openalex.org/W3186746298","https://openalex.org/W3195014212","https://openalex.org/W3198533054","https://openalex.org/W3201387179","https://openalex.org/W3211329537","https://openalex.org/W3216720075","https://openalex.org/W3217005392","https://openalex.org/W4210736635","https://openalex.org/W4213172700","https://openalex.org/W4312349930","https://openalex.org/W6684665197","https://openalex.org/W6803677459","https://openalex.org/W6810938606"],"related_works":["https://openalex.org/W4367313141","https://openalex.org/W2004086023","https://openalex.org/W2733999579","https://openalex.org/W2110217573","https://openalex.org/W4283374591","https://openalex.org/W2910751785","https://openalex.org/W4366547507","https://openalex.org/W3137434606","https://openalex.org/W4372263373","https://openalex.org/W3126438511"],"abstract_inverted_index":{"Building":[0],"footprint":[1,16],"is":[2,24,33],"a":[3,35,62,74],"key":[4],"indicator":[5],"of":[6,14,25,164],"urban":[7],"structures":[8],"and":[9,43,45,54,77,97,106,129,150],"economic":[10],"development.":[11],"Automatic":[12],"extraction":[13,57],"building":[15,125,133],"from":[17,167],"very-high-resolution":[18],"(VHR)":[19],"remote":[20],"sensing":[21],"imagery,":[22],"which":[23,70,156],"great":[26],"practical":[27],"interest":[28],"for":[29,38,85,152],"various":[30],"geospatial-related":[31],"applications,":[32],"still":[34],"challenging":[36],"task":[37,163],"complex":[39],"textures,":[40],"varying":[41],"scales":[42],"shapes,":[44],"other":[46,137],"confusing":[47],"artificial":[48],"objects.":[49],"To":[50],"alleviate":[51],"these":[52],"problems":[53],"improve":[55],"the":[56,82,86,103,108,112,121,143,153,162],"accuracy,":[58],"this":[59],"study":[60],"proposed":[61,116],"novel":[63],"pure":[64],"convolutional":[65],"neural":[66],"network":[67,109],"called":[68],"ConvBNet,":[69],"integrates":[71],"ConvNeXt-XL":[72],"with":[73,136],"fusing":[75],"decoder":[76],"adopts":[78],"deep":[79],"supervision":[80],"in":[81,161],"training":[83,90],"stage":[84],"middle":[87],"stage.":[88],"Two-stage":[89],"strategy,":[91],"using":[92],"weighted":[93],"cross-entropy":[94],"(CE)":[95],"loss":[96],"mask":[98],"CE":[99],"loss,":[100],"respectively,":[101],"ensures":[102],"stable":[104],"convergence":[105],"makes":[107],"focus":[110],"on":[111,120],"boundary":[113],"region.":[114],"The":[115],"model":[117],"was":[118],"tested":[119],"Wuhan":[122],"University":[123],"(WHU)":[124],"dataset":[126],"(publicly":[127],"available)":[128],"one":[130],"private":[131],"Zhejiang":[132],"dataset.":[134],"Compared":[135],"state-of-the-art":[138],"(SOTA)":[139],"methods,":[140],"ConvBNet":[141],"achieved":[142],"best":[144],"intersection":[145],"over":[146],"Union":[147],"(IoU),":[148],"91.22%":[149],"77.90%":[151],"two":[154],"datasets,":[155],"proves":[157],"its":[158],"good":[159],"performance":[160],"extracting":[165],"buildings":[166],"VHR":[168],"images.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
