{"id":"https://openalex.org/W3205123177","doi":"https://doi.org/10.1109/igarss47720.2021.9554123","title":"Self-Supervised Image Colorization for Semantic Segmentation of Urban Land Cover","display_name":"Self-Supervised Image Colorization for Semantic Segmentation of Urban Land Cover","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3205123177","doi":"https://doi.org/10.1109/igarss47720.2021.9554123","mag":"3205123177"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-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/A5022930273","display_name":"Jonathan Gonz\u00e1lez-Santiago","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111500","display_name":"Fraunhofer Institute of Optronics, System Technologies and Image Exploitation","ror":"https://ror.org/01zx97922","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210111500","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Jonathan Gonzalez-Santiago","raw_affiliation_strings":["Fraunhofer IOSB, Gutleuthausstr. 1,Ettlingen,Germany,76275"],"affiliations":[{"raw_affiliation_string":"Fraunhofer IOSB, Gutleuthausstr. 1,Ettlingen,Germany,76275","institution_ids":["https://openalex.org/I4210111500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002890036","display_name":"Fabian Schenkel","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111500","display_name":"Fraunhofer Institute of Optronics, System Technologies and Image Exploitation","ror":"https://ror.org/01zx97922","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210111500","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabian Schenkel","raw_affiliation_strings":["Fraunhofer IOSB, Gutleuthausstr. 1,Ettlingen,Germany,76275"],"affiliations":[{"raw_affiliation_string":"Fraunhofer IOSB, Gutleuthausstr. 1,Ettlingen,Germany,76275","institution_ids":["https://openalex.org/I4210111500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075370224","display_name":"Wolfgang Middelmann","orcid":"https://orcid.org/0000-0002-3507-7396"},"institutions":[{"id":"https://openalex.org/I4210111500","display_name":"Fraunhofer Institute of Optronics, System Technologies and Image Exploitation","ror":"https://ror.org/01zx97922","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210111500","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wolfgang Middelmann","raw_affiliation_strings":["Fraunhofer IOSB, Gutleuthausstr. 1,Ettlingen,Germany,76275"],"affiliations":[{"raw_affiliation_string":"Fraunhofer IOSB, Gutleuthausstr. 1,Ettlingen,Germany,76275","institution_ids":["https://openalex.org/I4210111500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5022930273"],"corresponding_institution_ids":["https://openalex.org/I4210111500"],"apc_list":null,"apc_paid":null,"fwci":0.3262,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.53262233,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3468","last_page":"3471"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11144","display_name":"melanin and skin pigmentation","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11144","display_name":"melanin and skin pigmentation","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9635000228881836,"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"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9513999819755554,"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/computer-science","display_name":"Computer science","score":0.8035672903060913},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6741690635681152},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6619523167610168},{"id":"https://openalex.org/keywords/pretext","display_name":"Pretext","score":0.6163672208786011},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6146158576011658},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5258505940437317},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5043026208877563},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4938412308692932},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4555467665195465},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.44676482677459717},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4456409811973572},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44415223598480225},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.42267662286758423},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41891491413116455},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.417726993560791},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.35998839139938354},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23014885187149048},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07957246899604797},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07665830850601196}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8035672903060913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6741690635681152},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6619523167610168},{"id":"https://openalex.org/C2779627259","wikidata":"https://www.wikidata.org/wiki/Q779763","display_name":"Pretext","level":3,"score":0.6163672208786011},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6146158576011658},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5258505940437317},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5043026208877563},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4938412308692932},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4555467665195465},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.44676482677459717},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4456409811973572},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44415223598480225},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.42267662286758423},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41891491413116455},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.417726993560791},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35998839139938354},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23014885187149048},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07957246899604797},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07665830850601196},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"},{"id":"pmh:oai:fraunhofer.de:N-642393","is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-642393.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer IOSB","raw_type":"Conference Paper"},{"id":"pmh:oai:null:publica/412612","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/412612","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2155524176","https://openalex.org/W2326925005","https://openalex.org/W2395611524","https://openalex.org/W2607903463","https://openalex.org/W2892964097","https://openalex.org/W3023371261","https://openalex.org/W6639824700","https://openalex.org/W6701655646","https://openalex.org/W6736911880","https://openalex.org/W6754683619"],"related_works":["https://openalex.org/W161456234","https://openalex.org/W3123043866","https://openalex.org/W2765162471","https://openalex.org/W2367130511","https://openalex.org/W4235007455","https://openalex.org/W2354300066","https://openalex.org/W2996988663","https://openalex.org/W2276802262","https://openalex.org/W2089741817","https://openalex.org/W2358993285"],"abstract_inverted_index":{"The":[0,101,129,142],"task":[1,52,95,110],"of":[2,12,23,37,42,53,65,126,139,185],"semantic":[3,38,54,124],"segmentation":[4,55,153],"plays":[5],"a":[6,30,43,69,91,173,178],"central":[7],"role":[8],"in":[9,20,40,189],"the":[10,21,35,51,84,146,165,183],"analysis":[11],"remotely":[13],"sensed":[14],"imagery.":[15],"This":[16,33,155],"relevance":[17],"is":[18,56],"reflected":[19],"act":[22],"classifying":[24],"each":[25],"image":[26],"pixel":[27],"belonging":[28],"to":[29,67,111,119,171,182],"particular":[31],"class.":[32],"allows":[34],"acquisition":[36],"knowledge":[39],"form":[41],"classification":[44,140],"map,":[45],"which":[46,72,115,176],"facilitates":[47],"decision-making":[48],"processes.":[49],"Nowadays,":[50],"mainly":[57],"solved":[58],"with":[59],"Supervised":[60],"pre-training.":[61],"It":[62],"needs":[63],"plenty":[64],"labels":[66],"learn":[68,112],"mapping":[70],"function,":[71],"produces":[73],"useful":[74],"features.":[75],"As":[76],"alternative,":[77],"Self-supervised":[78],"learning":[79],"(SSL)":[80],"techniques":[81],"entirely":[82],"explore":[83],"data,":[85],"find":[86],"supervision":[87],"signals":[88],"and":[89,137,164],"solve":[90],"challenge":[92],"called":[93],"Pretext":[94,109],"for":[96,122,135],"coming":[97],"upon":[98],"robust":[99],"representations.":[100],"current":[102],"work":[103],"investigates":[104],"Image":[105],"Colorization":[106],"(IC)":[107],"as":[108],"feature":[113],"representations,":[114],"will":[116],"be":[117],"transferred":[118],"an":[120],"U-Net":[121],"predicting":[123],"segmentations":[125],"urban":[127],"scenes.":[128],"study":[130],"examines":[131],"two":[132],"benchmark":[133],"datasets":[134],"validation":[136],"generation":[138],"maps.":[141],"results":[143],"show":[144],"that":[145],"learned":[147],"features":[148],"through":[149],"colorization":[150],"achieve":[151],"accurate":[152],"results.":[154],"was":[156],"possible":[157],"both":[158],"using":[159],"unlabeled":[160],"ImageNet":[161],"training":[162],"data":[163],"actual":[166],"datasets.":[167],"These":[168],"contain":[169],"up":[170],"half":[172],"million":[174],"examples,":[175],"represents":[177],"modest":[179],"amount":[180],"compared":[181],"number":[184],"annotated":[186],"images":[187],"present":[188],"ImageNet.":[190]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
