{"id":"https://openalex.org/W3214730767","doi":"https://doi.org/10.1145/3486635.3491069","title":"Semantic Segmentation in Aerial Images Using Class-Aware Unsupervised Domain Adaptation","display_name":"Semantic Segmentation in Aerial Images Using Class-Aware Unsupervised Domain Adaptation","publication_year":2021,"publication_date":"2021-11-02","ids":{"openalex":"https://openalex.org/W3214730767","doi":"https://doi.org/10.1145/3486635.3491069","mag":"3214730767"},"language":"en","primary_location":{"id":"doi:10.1145/3486635.3491069","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3486635.3491069","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3486635.3491069","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3486635.3491069","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027016896","display_name":"Ying Chen","orcid":"https://orcid.org/0000-0002-1914-8780"},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Chen","raw_affiliation_strings":["Illinois Institute of Technology, Chicago, Illinois, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology, Chicago, Illinois, United States","institution_ids":["https://openalex.org/I180949307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107222587","display_name":"Xu Ouyang","orcid":"https://orcid.org/0000-0003-2852-5733"},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xu Ouyang","raw_affiliation_strings":["Illinois Institute of Technology, Chicago, Illinois, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology, Chicago, Illinois, United States","institution_ids":["https://openalex.org/I180949307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060295702","display_name":"Kaiyue Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaiyue Zhu","raw_affiliation_strings":["Illinois Institute of Technology, Chicago, Illinois, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology, Chicago, Illinois, United States","institution_ids":["https://openalex.org/I180949307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023698748","display_name":"Gady Agam","orcid":null},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gady Agam","raw_affiliation_strings":["Illinois Institute of Technology, Chicago, Illinois, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology, Chicago, Illinois, United States","institution_ids":["https://openalex.org/I180949307"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I180949307"],"apc_list":null,"apc_paid":null,"fwci":0.4396,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6369993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"9","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9944999814033508,"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":0.9904999732971191,"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/computer-science","display_name":"Computer science","score":0.793149471282959},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7747135758399963},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7623292207717896},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6275659203529358},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5641404390335083},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5500326752662659},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4910021126270294},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.46007686853408813},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4363987445831299},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4355807602405548},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4254283010959625},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.360387921333313},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33316606283187866},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.1247357726097107},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10086244344711304},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0715053379535675}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.793149471282959},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7747135758399963},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7623292207717896},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6275659203529358},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5641404390335083},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5500326752662659},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4910021126270294},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.46007686853408813},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4363987445831299},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4355807602405548},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4254283010959625},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.360387921333313},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33316606283187866},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.1247357726097107},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10086244344711304},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0715053379535675},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3486635.3491069","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3486635.3491069","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3486635.3491069","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3486635.3491069","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3486635.3491069","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3486635.3491069","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3214730767.pdf","grobid_xml":"https://content.openalex.org/works/W3214730767.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W586034241","https://openalex.org/W1903029394","https://openalex.org/W1965555277","https://openalex.org/W1995875735","https://openalex.org/W2137825550","https://openalex.org/W2194775991","https://openalex.org/W2293363371","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2431874326","https://openalex.org/W2487365028","https://openalex.org/W2593768305","https://openalex.org/W2605287558","https://openalex.org/W2611292810","https://openalex.org/W2620086128","https://openalex.org/W2743242403","https://openalex.org/W2778764040","https://openalex.org/W2798376494","https://openalex.org/W2798964604","https://openalex.org/W2803297029","https://openalex.org/W2886742956","https://openalex.org/W2895281799","https://openalex.org/W2912327653","https://openalex.org/W2940597502","https://openalex.org/W2949987290","https://openalex.org/W2950361018","https://openalex.org/W2950373644","https://openalex.org/W2951670162","https://openalex.org/W2962793481","https://openalex.org/W2962808524","https://openalex.org/W2962976523","https://openalex.org/W2963073217","https://openalex.org/W2963107255","https://openalex.org/W2963120918","https://openalex.org/W2963250052","https://openalex.org/W2963858297","https://openalex.org/W2963865469","https://openalex.org/W2963958441","https://openalex.org/W2964228922","https://openalex.org/W2964278684","https://openalex.org/W2964285681","https://openalex.org/W2972285644","https://openalex.org/W2975672703","https://openalex.org/W2981512393","https://openalex.org/W2987632805","https://openalex.org/W3004969510","https://openalex.org/W3096541549","https://openalex.org/W3105672835","https://openalex.org/W3168470487","https://openalex.org/W4212774754"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2055243143","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4304166257","https://openalex.org/W4294635752","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W4321636575"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,93,142],"using":[2,17,64,139],"deep":[3],"neural":[4],"networks":[5],"is":[6,45],"an":[7],"important":[8],"component":[9],"of":[10,91,94,104,135],"aerial":[11,48,95],"image":[12],"understanding.":[13],"However,":[14],"models":[15],"trained":[16,66],"data":[18,35],"from":[19],"one":[20],"domain":[21,28,32,43,81,86,105,126],"may":[22,61],"not":[23],"generalize":[24],"well":[25],"to":[26,30,51,84,127],"another":[27],"due":[29,50],"a":[31,42,65,78,140],"shift":[33,87,106],"between":[34,112],"distributions":[36],"in":[37,47,88],"the":[38,89,102,113,124,133,136],"two":[39],"domains.":[40,117],"Such":[41],"gap":[44],"common":[46],"images":[49],"large":[52],"visual":[53],"appearance":[54],"changes,":[55],"and":[56,115,146],"so":[57],"substantial":[58],"accuracy":[59],"loss":[60],"occur":[62],"when":[63],"model":[67],"for":[68],"inference":[69],"on":[70,123],"new":[71],"data.":[72],"In":[73],"this":[74,98],"paper,":[75],"we":[76,100,119],"propose":[77],"novel":[79],"unsupervised":[80],"adaptation":[82],"framework":[83],"address":[85,101],"context":[90],"semantic":[92],"images.":[96],"To":[97],"end,":[99],"problem":[103],"by":[107,144],"learning":[108],"class-aware":[109],"distribution":[110],"differences":[111],"source":[114],"target":[116,125],"Further,":[118],"employ":[120],"entropy":[121],"minimization":[122],"produce":[128],"high-confidence":[129],"predictions.":[130],"We":[131],"demonstrate":[132],"effectiveness":[134],"proposed":[137],"approach":[138],"challenge":[141],"dataset":[143],"ISPRS,":[145],"show":[147],"improvement":[148],"over":[149],"state-of-the-art":[150],"methods.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
