{"id":"https://openalex.org/W4291913402","doi":"https://doi.org/10.3390/rs14163914","title":"MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery","display_name":"MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4291913402","doi":"https://doi.org/10.3390/rs14163914"},"language":"en","primary_location":{"id":"doi:10.3390/rs14163914","doi":"https://doi.org/10.3390/rs14163914","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14163914","pdf_url":"https://www.mdpi.com/2072-4292/14/16/3914/pdf?version=1660543831","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/14/16/3914/pdf?version=1660543831","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073933345","display_name":"Yue Qiu","orcid":"https://orcid.org/0000-0002-4984-4920","observed_orcids":["https://orcid.org/0000-0002-4984-4920","https://orcid.org/0009-0004-9557-2905"]},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Qiu","raw_affiliation_strings":["Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"],"raw_orcid":"https://orcid.org/0000-0002-4984-4920","affiliations":[{"raw_affiliation_string":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101397729","display_name":"Fang Wu","orcid":"https://orcid.org/0000-0001-8431-3079","observed_orcids":["https://orcid.org/0000-0001-8431-3079"]},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fang Wu","raw_affiliation_strings":["Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052883222","display_name":"Jichong Yin","orcid":"https://orcid.org/0000-0001-9690-6373","observed_orcids":["https://orcid.org/0000-0001-9690-6373"]},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jichong Yin","raw_affiliation_strings":["Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"],"raw_orcid":"https://orcid.org/0000-0001-9690-6373","affiliations":[{"raw_affiliation_string":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101940197","display_name":"Chengyi Liu","orcid":"https://orcid.org/0009-0002-8940-9787","observed_orcids":["https://orcid.org/0009-0002-8940-9787"]},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengyi Liu","raw_affiliation_strings":["Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081393853","display_name":"Xianyong Gong","orcid":"https://orcid.org/0000-0002-6184-1300","observed_orcids":["https://orcid.org/0000-0002-6184-1300"]},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianyong Gong","raw_affiliation_strings":["Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"],"raw_orcid":"https://orcid.org/0000-0002-6184-1300","affiliations":[{"raw_affiliation_string":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115695267","display_name":"Andong Wang","orcid":"https://orcid.org/0009-0009-9848-1650","observed_orcids":["https://orcid.org/0009-0009-9848-1650","https://orcid.org/0000-0003-0822-3324"]},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Andong Wang","raw_affiliation_strings":["Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China","institution_ids":["https://openalex.org/I169689159"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101397729"],"corresponding_institution_ids":["https://openalex.org/I169689159"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2618},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2618},"fwci":1.671,"has_fulltext":true,"cited_by_count":30,"citation_normalized_percentile":{"value":0.85117737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"14","issue":"16","first_page":"3914","last_page":"3914"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9994999766349792,"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":0.9994999766349792,"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":0.0003000000142492354,"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":0.0,"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/building-extraction","display_name":"building extraction","score":0.9940000176429749},{"id":"https://openalex.org/keywords/aerial-imagery","display_name":"aerial imagery","score":0.9739999771118164},{"id":"https://openalex.org/keywords/depthwise-separable-convolution","display_name":"depthwise separable convolution","score":0.9129999876022339},{"id":"https://openalex.org/keywords/group-normalization","display_name":"group normalization","score":0.9559999704360962},{"id":"https://openalex.org/keywords/atrous-spatial-pyramid-pooling","display_name":"atrous spatial pyramid pooling","score":0.9909999966621399},{"id":"https://openalex.org/keywords/multiscale-feature-fusion","display_name":"multi-scale feature fusion","score":0.7419999837875366},{"id":"https://openalex.org/keywords/deformable-convolution","display_name":"deformable convolution","score":0.9350000023841858},{"id":"https://openalex.org/keywords/pixel-based-classification","display_name":"pixel classification","score":0.6140000224113464}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7865429520606995},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.6744366884231567},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6249712705612183},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5484047532081604},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.5213157534599304},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5094082951545715},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47822898626327515},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.4539385735988617},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.445590078830719},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4292307496070862},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4043707847595215},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3630320727825165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35178565979003906},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28036582469940186},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs14163914","doi":"https://doi.org/10.3390/rs14163914","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14163914","pdf_url":"https://www.mdpi.com/2072-4292/14/16/3914/pdf?version=1660543831","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2a6ede41f6d649ecb2e0853aacaf3c5e","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/2a6ede41f6d649ecb2e0853aacaf3c5e","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":"Remote Sensing, Vol 14, Iss 16, p 3914 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs14163914","doi":"https://doi.org/10.3390/rs14163914","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14163914","pdf_url":"https://www.mdpi.com/2072-4292/14/16/3914/pdf?version=1660543831","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5142999887466431,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4291913402.pdf","grobid_xml":"https://content.openalex.org/works/W4291913402.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W1579869106","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1974526806","https://openalex.org/W2045366687","https://openalex.org/W2047254338","https://openalex.org/W2085665642","https://openalex.org/W2107625686","https://openalex.org/W2124641210","https://openalex.org/W2126092053","https://openalex.org/W2194775991","https://openalex.org/W2371735103","https://openalex.org/W2523500206","https://openalex.org/W2533566148","https://openalex.org/W2536910603","https://openalex.org/W2551751523","https://openalex.org/W2560023338","https://openalex.org/W2609402060","https://openalex.org/W2623331213","https://openalex.org/W2755226765","https://openalex.org/W2791075437","https://openalex.org/W2794606897","https://openalex.org/W2795783309","https://openalex.org/W2884561390","https://openalex.org/W2908320224","https://openalex.org/W2939647427","https://openalex.org/W2952956606","https://openalex.org/W2963163009","https://openalex.org/W2964309882","https://openalex.org/W2966926453","https://openalex.org/W2984723538","https://openalex.org/W2987361643","https://openalex.org/W2991954627","https://openalex.org/W3014060899","https://openalex.org/W3034328552","https://openalex.org/W3088431851","https://openalex.org/W3096168491","https://openalex.org/W3112979587","https://openalex.org/W3122259118","https://openalex.org/W3159155580","https://openalex.org/W3186363826","https://openalex.org/W3215100961","https://openalex.org/W3217005392","https://openalex.org/W4224055292","https://openalex.org/W4225359650","https://openalex.org/W4312784337","https://openalex.org/W4382706200","https://openalex.org/W4382897005"],"related_works":["https://openalex.org/W3148283428","https://openalex.org/W2938425859","https://openalex.org/W2277790332","https://openalex.org/W2755226765","https://openalex.org/W2981007796","https://openalex.org/W3193128306","https://openalex.org/W7214179490","https://openalex.org/W2888733778","https://openalex.org/W4391539214","https://openalex.org/W4387145990"],"abstract_inverted_index":{"There":[0],"remains":[1],"several":[2],"challenges":[3],"that":[4,62,174,187],"are":[5],"encountered":[6],"in":[7,48,95,266],"the":[8,22,44,83,96,135,147,153,192,206],"task":[9],"of":[10,25,134,157,188,226,236],"extracting":[11],"buildings":[12,160],"from":[13],"aerial":[14],"imagery":[15],"using":[16],"convolutional":[17],"neural":[18],"networks":[19,29,40],"(CNNs).":[20],"First,":[21,73],"tremendous":[23],"complexity":[24],"existing":[26],"building":[27,46,67,109,267],"extraction":[28,155,268],"impedes":[30],"their":[31],"practical":[32],"application.":[33],"In":[34],"addition,":[35],"it":[36],"is":[37],"arduous":[38],"for":[39,159,232,264],"to":[41,80,100,124,128,151],"sufficiently":[42],"utilize":[43],"various":[45],"features":[47,68,110,130],"different":[49,132],"images.":[50],"To":[51],"address":[52],"these":[53],"challenges,":[54],"we":[55,74,88,106,139],"propose":[56],"an":[57,112,223,233,244,250],"efficient":[58],"network":[59,84,102,144],"called":[60],"MSL-Net":[61,158,194,259],"focuses":[63],"on":[64,168,205,243],"both":[65],"multiscale":[66,108],"and":[69,86,119,198,217,255],"multilevel":[70],"image":[71,235],"features.":[72],"use":[75,265],"depthwise":[76],"separable":[77],"convolution":[78,143],"(DSC)":[79],"significantly":[81],"reduce":[82],"complexity,":[85],"then":[87],"embed":[89],"a":[90,141,182],"group":[91],"normalization":[92],"(GN)":[93],"layer":[94,145],"inverted":[97],"residual":[98],"structure":[99],"alleviate":[101],"performance":[103],"degradation.":[104],"Furthermore,":[105],"extract":[107],"through":[111],"atrous":[113],"spatial":[114],"pyramid":[115],"pooling":[116],"(ASPP)":[117],"module":[118],"apply":[120],"long":[121],"skip":[122],"connections":[123],"establish":[125],"long-distance":[126],"dependence":[127],"fuse":[129],"at":[131],"levels":[133],"given":[136],"image.":[137],"Finally,":[138],"add":[140],"deformable":[142],"before":[146],"pixel":[148],"classification":[149],"step":[150],"enhance":[152],"feature":[154],"capability":[156],"with":[161,181,222],"irregular":[162],"shapes.":[163],"The":[164],"experimental":[165],"results":[166],"obtained":[167],"three":[169],"publicly":[170],"available":[171],"datasets":[172],"demonstrate":[173],"our":[175,257],"proposed":[176,193,258],"method":[177],"achieves":[178,195],"state-of-the-art":[179],"accuracy":[180,254],"faster":[183],"inference":[184,224],"speed":[185,225],"than":[186],"competing":[189],"approaches.":[190],"Specifically,":[191],"90.4%,":[196],"81.1%":[197],"70.9%":[199],"intersection":[200],"over":[201],"union":[202],"(IoU)":[203],"values":[204],"WHU":[207],"Building":[208],"Aerial":[209,213],"Imagery":[210],"dataset,":[211,220],"Inria":[212],"Image":[214],"Labeling":[215],"dataset":[216],"Massachusetts":[218],"Buildings":[219],"respectively,":[221],"101.4":[227],"frames":[228],"per":[229],"second":[230],"(FPS)":[231],"input":[234],"size":[237],"3":[238],"\u00d7":[239,241],"512":[240,242],"NVIDIA":[245],"RTX":[246],"3090":[247],"GPU.":[248],"With":[249],"excellent":[251],"tradeoff":[252],"between":[253],"speed,":[256],"may":[260],"hold":[261],"great":[262],"promise":[263],"tasks.":[269]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":3}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2022-08-16T00:00:00"}