{"id":"https://openalex.org/W3084438775","doi":"https://doi.org/10.1109/lgrs.2020.3018858","title":"Fine-Grained Building Change Detection From Very High-Spatial-Resolution Remote Sensing Images Based on Deep Multitask Learning","display_name":"Fine-Grained Building Change Detection From Very High-Spatial-Resolution Remote Sensing Images Based on Deep Multitask Learning","publication_year":2020,"publication_date":"2020-09-07","ids":{"openalex":"https://openalex.org/W3084438775","doi":"https://doi.org/10.1109/lgrs.2020.3018858","mag":"3084438775"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3018858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3018858","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/A5057519471","display_name":"Ying Sun","orcid":"https://orcid.org/0000-0002-9350-021X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Sun","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9350-021X","affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037230519","display_name":"Xinchang Zhang","orcid":"https://orcid.org/0000-0001-8463-9757"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinchang Zhang","raw_affiliation_strings":["School of Geographical Sciences, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8463-9757","affiliations":[{"raw_affiliation_string":"School of Geographical Sciences, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089622672","display_name":"Jianfeng Huang","orcid":"https://orcid.org/0000-0002-1940-5708"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianfeng Huang","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1940-5708","affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100379070","display_name":"Haiying Wang","orcid":"https://orcid.org/0000-0002-6679-9543"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiying Wang","raw_affiliation_strings":["College of Environment and Planning, Henan University, Kaifeng, China","Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Environment and Planning, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]},{"raw_affiliation_string":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030654256","display_name":"Qinchuan Xin","orcid":"https://orcid.org/0000-0003-1146-4874"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210101675","display_name":"Research Center for Ecology and Environment of Central Asia","ror":"https://ror.org/00q2dta10","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210101675","https://openalex.org/I4210103115"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinchuan Xin","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","State Key Laboratory of Desert and Oasis Ecology, Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China"],"raw_orcid":"https://orcid.org/0000-0003-1146-4874","affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"State Key Laboratory of Desert and Oasis Ecology, Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210101675"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3697,"has_fulltext":false,"cited_by_count":76,"citation_normalized_percentile":{"value":0.93578703,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991999864578247,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.984499990940094,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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":0.7006848454475403},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6344382762908936},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5960148572921753},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5712422728538513},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.5590596795082092},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47504425048828125},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3881058394908905},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.19333991408348083}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7006848454475403},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6344382762908936},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5960148572921753},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5712422728538513},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.5590596795082092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47504425048828125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3881058394908905},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.19333991408348083}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2020.3018858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3018858","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":0.8100000023841858,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G1635589568","display_name":null,"funder_award_id":"2017YFA0604300","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G286069986","display_name":null,"funder_award_id":"19lgpy44","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G2981101543","display_name":null,"funder_award_id":"41801351","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4877365600","display_name":null,"funder_award_id":"2017YFA0604400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G4916234390","display_name":null,"funder_award_id":"41875122","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"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W76086797","https://openalex.org/W172260869","https://openalex.org/W1896424170","https://openalex.org/W1901129140","https://openalex.org/W1964669384","https://openalex.org/W2079589766","https://openalex.org/W2085289201","https://openalex.org/W2134451879","https://openalex.org/W2170260354","https://openalex.org/W2205574202","https://openalex.org/W2295862745","https://openalex.org/W2323766577","https://openalex.org/W2529419676","https://openalex.org/W2547811817","https://openalex.org/W2610884537","https://openalex.org/W2752414286","https://openalex.org/W2774218952","https://openalex.org/W2884276099","https://openalex.org/W2888697548","https://openalex.org/W2904696994","https://openalex.org/W2931695950","https://openalex.org/W2935584708","https://openalex.org/W2943781490","https://openalex.org/W2963881378","https://openalex.org/W2966542792","https://openalex.org/W3099831940","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W2568858292","https://openalex.org/W1515964938","https://openalex.org/W2389381914","https://openalex.org/W2376528221","https://openalex.org/W196800607","https://openalex.org/W4375867731","https://openalex.org/W2359428812","https://openalex.org/W3181296946","https://openalex.org/W2015705630","https://openalex.org/W2369060955"],"abstract_inverted_index":{"Building":[0],"change":[1,78,104,112,136,148,174,178,238],"detection":[2,79,113,137,149,168,179,239],"from":[3,32,41,121],"very":[4],"high-spatial-resolution":[5],"(VHR)":[6],"remote":[7],"sensing":[8],"images":[9,44,198],"has":[10],"gained":[11],"increasing":[12],"popularity":[13],"in":[14,77,193],"a":[15],"variety":[16],"of":[17,39,65,71,92,98,135,142,164,173],"applications,":[18],"such":[19],"as":[20,48,94],"urban":[21],"planning":[22],"and":[23,68,82,130,138,160,204,230,235],"damage":[24],"assessment.":[25],"Detecting":[26],"fine-grained":[27,102,227],"\u201cfrom\u2013to\u201d":[28,103,228],"changes":[29,120,229],"(change":[30],"transition":[31],"one":[33],"land":[34],"cover":[35],"type":[36],"to":[37,62],"another)":[38],"buildings":[40,93],"the":[42,89,101,122,127,132,139,147,155,162,165,171,183,195,216,222,232,236],"VHR":[43,72,123],"is":[45,51,80,115,152,187],"still":[46],"challenging":[47],"multitemporal":[49],"representation":[50],"complicated.":[52],"Recently,":[53],"fully":[54],"convolutional":[55],"neural":[56],"networks":[57],"(FCNs)":[58],"have":[59],"been":[60],"proven":[61],"be":[63],"capable":[64],"feature":[66],"extraction":[67],"semantic":[69,90,143,157],"segmentation":[70,91,144,158],"images,":[73],"but":[74],"its":[75],"ability":[76],"untested":[81],"unknown.":[83],"In":[84],"this":[85],"letter,":[86],"we":[87],"leverage":[88],"an":[95],"auxiliary":[96,140,156],"source":[97],"information":[99],"for":[100,111,117,170,190],"detection.":[105,175],"A":[106,176],"deep":[107],"multitask":[108],"learning":[109],"framework":[110],"(MTL-CD)":[114],"proposed":[116],"detecting":[118],"building":[119,166,177],"images.":[124],"MTL-CD":[125,223],"adopts":[126],"encoder\u2013decoder":[128],"architecture":[129],"solves":[131],"main":[133],"task":[134],"tasks":[141,159],"simultaneously.":[145],"Accordingly,":[146],"loss":[150],"function":[151],"constrained":[153],"by":[154,201],"enables":[161],"back-propagation":[163],"footprints\u2019":[167],"errors":[169],"improvement":[172],"data":[180,185,218],"set":[181,186,219],"named":[182],"Guangzhou":[184,217],"also":[188],"developed":[189],"model":[191],"evaluation,":[192],"which":[194],"bitemporal":[196],"R\u2013G\u2013B":[197],"were":[199],"collected":[200],"airplane":[202],"(2009)":[203],"unmanned":[205],"aerial":[206],"vehicle":[207],"(UAV,":[208],"2019)":[209],"with":[210],"different":[211],"flight":[212],"heights.":[213],"Experiments":[214],"on":[215],"demonstrate":[220],"that":[221],"method":[224],"effectively":[225],"detects":[226],"outperforms":[231],"postclassification":[233],"methods":[234],"direct":[237],"methods.":[240]},"counts_by_year":[{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}