{"id":"https://openalex.org/W2974382310","doi":"https://doi.org/10.3390/rs11182142","title":"Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images","display_name":"Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images","publication_year":2019,"publication_date":"2019-09-14","ids":{"openalex":"https://openalex.org/W2974382310","doi":"https://doi.org/10.3390/rs11182142","mag":"2974382310"},"language":"en","primary_location":{"id":"doi:10.3390/rs11182142","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11182142","pdf_url":"https://www.mdpi.com/2072-4292/11/18/2142/pdf?version=1568510802","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,"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"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/11/18/2142/pdf?version=1568510802","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019176178","display_name":"Lianfa Li","orcid":"https://orcid.org/0000-0002-9382-8637"},"institutions":[{"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/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lianfa Li","raw_affiliation_strings":["State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100101, China","University of Chinese Academy of Sciences, Beijing 100049, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100101, China","institution_ids":["https://openalex.org/I4210160793","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5019176178"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210160793","https://openalex.org/I4210165038"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.0424,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.8991851,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"11","issue":"18","first_page":"2142","last_page":"2142"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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":0.9987999796867371,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9976999759674072,"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":0.8256926536560059},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7645511627197266},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7577855587005615},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.74146568775177},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6562694311141968},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.63206547498703},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5348913073539734},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4959181249141693},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4464190602302551},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4276532530784607},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4255119860172272},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.42374932765960693},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08003285527229309}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8256926536560059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7645511627197266},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7577855587005615},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.74146568775177},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6562694311141968},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.63206547498703},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5348913073539734},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4959181249141693},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4464190602302551},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4276532530784607},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4255119860172272},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.42374932765960693},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08003285527229309},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs11182142","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11182142","pdf_url":"https://www.mdpi.com/2072-4292/11/18/2142/pdf?version=1568510802","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,"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:a0974827473245d3986480afa3b28f58","is_oa":true,"landing_page_url":"https://doaj.org/article/a0974827473245d3986480afa3b28f58","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"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 11, Iss 18, p 2142 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/11/18/2142/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs11182142","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 11; Issue 18; Pages: 2142","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs11182142","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11182142","pdf_url":"https://www.mdpi.com/2072-4292/11/18/2142/pdf?version=1568510802","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,"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":[],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1527255440","display_name":null,"funder_award_id":"XDA19040501","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G352119645","display_name":null,"funder_award_id":"XDA19040501","funder_id":"https://openalex.org/F4320321133","funder_display_name":"Chinese Academy of Sciences"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6258415954","display_name":null,"funder_award_id":"Chinese","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7196491996","display_name":null,"funder_award_id":"41471376","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8208342437","display_name":null,"funder_award_id":"1 and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321133","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2974382310.pdf","grobid_xml":"https://content.openalex.org/works/W2974382310.grobid-xml"},"referenced_works_count":79,"referenced_works":["https://openalex.org/W1586730761","https://openalex.org/W1745334888","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1905829557","https://openalex.org/W1937812750","https://openalex.org/W1949049686","https://openalex.org/W1973749534","https://openalex.org/W1984309565","https://openalex.org/W1993109692","https://openalex.org/W2022508996","https://openalex.org/W2022532533","https://openalex.org/W2053757129","https://openalex.org/W2060276164","https://openalex.org/W2075948878","https://openalex.org/W2082332749","https://openalex.org/W2085411191","https://openalex.org/W2097117768","https://openalex.org/W2100588357","https://openalex.org/W2107640784","https://openalex.org/W2116877738","https://openalex.org/W2124592697","https://openalex.org/W2142834259","https://openalex.org/W2145339207","https://openalex.org/W2150418119","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2404464147","https://openalex.org/W2412588858","https://openalex.org/W2412782625","https://openalex.org/W2489529491","https://openalex.org/W2519537448","https://openalex.org/W2528639018","https://openalex.org/W2541674938","https://openalex.org/W2545985378","https://openalex.org/W2550143307","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2592929672","https://openalex.org/W2598666589","https://openalex.org/W2632026579","https://openalex.org/W2737129951","https://openalex.org/W2741295496","https://openalex.org/W2758404062","https://openalex.org/W2764034829","https://openalex.org/W2774320778","https://openalex.org/W2777186991","https://openalex.org/W2778188318","https://openalex.org/W2782522152","https://openalex.org/W2791567908","https://openalex.org/W2792559311","https://openalex.org/W2792711805","https://openalex.org/W2794842021","https://openalex.org/W2798643036","https://openalex.org/W2799406003","https://openalex.org/W2804860796","https://openalex.org/W2805828795","https://openalex.org/W2866634454","https://openalex.org/W2890672676","https://openalex.org/W2902540630","https://openalex.org/W2912863798","https://openalex.org/W2919115771","https://openalex.org/W2940726923","https://openalex.org/W2949545510","https://openalex.org/W2958449090","https://openalex.org/W2962727772","https://openalex.org/W2962850830","https://openalex.org/W2963753570","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W3100521496","https://openalex.org/W3122507398","https://openalex.org/W4205947740","https://openalex.org/W4298082496","https://openalex.org/W4388297464","https://openalex.org/W6638908295","https://openalex.org/W6677296758","https://openalex.org/W6698444068"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2953234277","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,29,65,89,119,223,286],"is":[2,132],"a":[3,99,128,135],"fundamental":[4],"means":[5],"of":[6,27,30,58,66,84,90,104,111,120,166,178,224,231,287,294],"extracting":[7],"information":[8,195],"from":[9,36,196],"remotely":[10,79,91,121,232,295],"sensed":[11,80,92,122,233,296],"images":[12,70,93,199],"at":[13],"the":[14,50,155,164,171,197,202,214,221,238,242,247,256,263,274],"pixel":[15],"level.":[16],"Deep":[17],"learning":[18,86,106,113,172,209,272],"has":[19],"enabled":[20],"considerable":[21],"improvements":[22],"in":[23,139,158,220,227,255,262],"efficiency":[24,173],"and":[25,45,71,73,114,146,150,169,210,259,265,282,290],"accuracy":[26],"semantic":[28,88,118,222,285],"general":[31,67],"images.":[32,81,124,234,297],"Typical":[33],"models":[34,60],"range":[35],"benchmarks":[37],"such":[38],"as":[39,218],"fully":[40,136],"convolutional":[41,137],"networks,":[42],"U-Net,":[43,159,237],"Micro-Net,":[44],"dilated":[46],"residual":[47,112,130,141,161,208],"networks":[48],"to":[49,78,134,192,200,204,284],"more":[51],"recently":[52],"developed":[53,63],"DeepLab":[54],"3+.":[55],"However,":[56],"many":[57],"these":[59,160],"were":[61],"originally":[62],"for":[64,87,116,281],"or":[68,183,246],"medical":[69],"videos,":[72],"are":[74,94,143],"not":[75],"directly":[76],"relevant":[77],"The":[82,207],"studies":[83],"deep":[85,105,129,271],"limited.":[95],"This":[96],"paper":[97],"presents":[98],"novel":[100],"flexible":[101,270],"autoencoder-based":[102],"architecture":[103],"that":[107],"makes":[108],"extensive":[109,176],"use":[110],"multiscaling":[115,211],"robust":[117],"land-use":[123,225,288],"In":[125,180],"this":[126],"architecture,":[127,273],"autoencoder":[131],"generalized":[133],"network":[138],"which":[140],"connections":[142,162],"implemented":[144],"within":[145],"between":[147],"all":[148],"encoding":[149],"decoding":[151],"layers.":[152],"Compared":[153,235],"with":[154,236],"concatenated":[156],"shortcuts":[157],"reduce":[163],"number":[165],"trainable":[167],"parameters":[168],"improve":[170,213],"by":[174,253,260],"enabling":[175],"backpropagation":[177],"errors.":[179],"addition,":[181],"resizing":[182],"atrous":[184],"spatial":[185],"pyramid":[186],"pooling":[187],"(ASPP)":[188],"can":[189,277],"be":[190,278],"leveraged":[191],"capture":[193],"multiscale":[194],"input":[198],"enhance":[201],"robustness":[203],"scale":[205],"variations.":[206],"strategies":[212],"trained":[215],"model\u2019s":[216],"generalizability,":[217],"demonstrated":[219],"types":[226],"two":[228],"real-world":[229],"datasets":[230],"proposed":[239,275],"method":[240],"improves":[241],"Jaccard":[243],"index":[244],"(JI)":[245],"mean":[248],"intersection":[249],"over":[250],"union":[251],"(MIoU)":[252],"4-11%":[254],"training":[257],"phase":[258],"3-9%":[261],"validation":[264],"testing":[266],"phases.":[267],"With":[268],"its":[269],"approach":[276],"easily":[279],"applied":[280],"transferred":[283],"variables":[289,293],"other":[291],"surface":[292]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":6}],"updated_date":"2026-04-19T08:26:33.389920","created_date":"2025-10-10T00:00:00"}