{"id":"https://openalex.org/W4312984783","doi":"https://doi.org/10.1109/tgrs.2022.3215852","title":"LCS: A Collaborative Optimization Framework of Vector Extraction and Semantic Segmentation for Building Extraction","display_name":"LCS: A Collaborative Optimization Framework of Vector Extraction and Semantic Segmentation for Building Extraction","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312984783","doi":"https://doi.org/10.1109/tgrs.2022.3215852"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3215852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3215852","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5078685387","display_name":"Zhengyu Liu","orcid":"https://orcid.org/0000-0003-0203-4426"},"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":true,"raw_author_name":"Zhengyu Liu","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","School of Geography and Planning, Sun Yet-Sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Geography and Planning, Sun Yet-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010856973","display_name":"Qian Shi","orcid":"https://orcid.org/0000-0002-1276-0352"},"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":"Qian Shi","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","School of Geography and Planning, Sun Yet-Sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Geography and Planning, Sun Yet-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012897233","display_name":"Jinpei Ou","orcid":null},"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":"Jinpei Ou","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","School of Geography and Planning, Sun Yet-Sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Geography and Planning, Sun Yet-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078685387"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":2.895,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.91144913,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9990000128746033,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9988999962806702,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9988999962806702,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8329305648803711},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7938404679298401},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7635074853897095},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5856154561042786},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5281707644462585},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46239861845970154},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.46067675948143005},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4339365065097809},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3990674614906311},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3649263083934784}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8329305648803711},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7938404679298401},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7635074853897095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5856154561042786},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5281707644462585},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46239861845970154},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.46067675948143005},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4339365065097809},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3990674614906311},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3649263083934784}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3215852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3215852","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6299999952316284,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G3828105410","display_name":null,"funder_award_id":"61976234","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4555523670","display_name":null,"funder_award_id":"2021A151501119","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G782352832","display_name":null,"funder_award_id":"42171410","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/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1536680647","https://openalex.org/W1755653603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1976827068","https://openalex.org/W2044609898","https://openalex.org/W2097117768","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2412782625","https://openalex.org/W2508741746","https://openalex.org/W2523500206","https://openalex.org/W2560023338","https://openalex.org/W2620689612","https://openalex.org/W2752782242","https://openalex.org/W2755226765","https://openalex.org/W2776462702","https://openalex.org/W2778539913","https://openalex.org/W2790741584","https://openalex.org/W2795635230","https://openalex.org/W2799213142","https://openalex.org/W2884436604","https://openalex.org/W2908320224","https://openalex.org/W2937933649","https://openalex.org/W2939647427","https://openalex.org/W2962914239","https://openalex.org/W2963091558","https://openalex.org/W2963446712","https://openalex.org/W2963525222","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W2981415272","https://openalex.org/W2982206001","https://openalex.org/W2991441757","https://openalex.org/W2996327453","https://openalex.org/W3035228270","https://openalex.org/W3053564872","https://openalex.org/W3104035745","https://openalex.org/W3109301572","https://openalex.org/W3110797472","https://openalex.org/W3112929693","https://openalex.org/W3122259118","https://openalex.org/W3126435384","https://openalex.org/W3129784683","https://openalex.org/W3132842068","https://openalex.org/W3156066202","https://openalex.org/W3157124226","https://openalex.org/W3169869794","https://openalex.org/W3171450708","https://openalex.org/W3176363936","https://openalex.org/W3184169161","https://openalex.org/W6631190155","https://openalex.org/W6639824700"],"related_works":["https://openalex.org/W1986655823","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W3011384228","https://openalex.org/W2124969951","https://openalex.org/W2945274617","https://openalex.org/W4313052709","https://openalex.org/W4298131179","https://openalex.org/W2375430703"],"abstract_inverted_index":{"In":[0,65,87],"the":[1,14,17,50,57,70,73,80,88,112,127,139,144,150],"field":[2],"of":[3,16,39,49,59,72,76,129,146],"building":[4],"extraction,":[5,99],"many":[6],"CNN-based":[7],"methods":[8,43,77,165],"have":[9],"been":[10],"developed":[11],"to":[12,27,166],"solve":[13],"problem":[15,58],"irregular":[18],"boundaries":[19],"in":[20,161],"their":[21,117],"predictions.":[22],"The":[23,120],"prevailing":[24],"approach":[25],"is":[26,135,155],"build":[28],"an":[29],"additional":[30],"edge":[31],"segmentation":[32,84,93,134],"branch":[33],"or":[34],"obtain":[35,46],"accurate":[36,47],"vector":[37,53,98,101,130,163],"components":[38],"buildings.":[40],"However,":[41],"pixel-based":[42],"still":[44],"cannot":[45],"location":[48,95],"boundary,":[51],"while":[52,100],"extraction":[54,102,131,164],"will":[55],"bring":[56],"sample":[60],"imbalance":[61],"and":[62,78,132,157],"missing":[63],"detection.":[64],"this":[66,110],"work,":[67],"we":[68],"utilize":[69],"complementarity":[71],"two":[74,113],"types":[75],"propose":[79],"line":[81],"segment":[82],"collaborate":[83],"(LCS)":[85],"framework.":[86],"proposed":[89],"LCS":[90,140],"framework,":[91,141],"semantic":[92,107,133],"provides":[94,103],"guidance":[96],"for":[97,106],"precise":[104],"positioning":[105],"segmentation.":[108],"By":[109],"way,":[111],"tasks":[114],"can":[115,158],"leverage":[116],"respective":[118],"strengths.":[119],"results":[121],"on":[122],"three":[123],"datasets":[124],"show":[125],"that":[126],"performance":[128],"improved":[136],"simultaneously":[137],"using":[138],"which":[142],"proves":[143],"effectiveness":[145],"our":[147,153],"method.":[148],"At":[149],"same":[151],"time,":[152],"framework":[154],"flexible":[156],"be":[159],"embedded":[160],"other":[162],"improve":[167],"performance.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":11}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}