{"id":"https://openalex.org/W3153636292","doi":"https://doi.org/10.3390/rs13081523","title":"Assessing Deep Convolutional Neural Networks and Assisted Machine Perception for Urban Mapping","display_name":"Assessing Deep Convolutional Neural Networks and Assisted Machine Perception for Urban Mapping","publication_year":2021,"publication_date":"2021-04-15","ids":{"openalex":"https://openalex.org/W3153636292","doi":"https://doi.org/10.3390/rs13081523","mag":"3153636292"},"language":"en","primary_location":{"id":"doi:10.3390/rs13081523","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13081523","pdf_url":"https://www.mdpi.com/2072-4292/13/8/1523/pdf?version=1618740275","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/2072-4292/13/8/1523/pdf?version=1618740275","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079984408","display_name":"Yang Shao","orcid":"https://orcid.org/0000-0002-4870-7406"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yang Shao","raw_affiliation_strings":["Department of Geography, Virginia Tech, 238 Wallace Hall, Blacksburg, VA 24060, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography, Virginia Tech, 238 Wallace Hall, Blacksburg, VA 24060, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022308778","display_name":"Austin J. Cooner","orcid":"https://orcid.org/0000-0001-8019-4455"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Austin J. Cooner","raw_affiliation_strings":["Department of Geography, Virginia Tech, 238 Wallace Hall, Blacksburg, VA 24060, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography, Virginia Tech, 238 Wallace Hall, Blacksburg, VA 24060, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075708008","display_name":"Stephen J. Walsh","orcid":"https://orcid.org/0000-0003-2783-7938"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stephen J. Walsh","raw_affiliation_strings":["Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3220, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3220, USA","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079984408"],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2784},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2784},"fwci":0.6863,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.69950017,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"13","issue":"8","first_page":"1523","last_page":"1523"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991000294685364,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991000294685364,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9987999796867371,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9973000288009644,"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":0.699305534362793},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6526038646697998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6200472712516785},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.5350348353385925},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.49805760383605957},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.42602983117103577},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4183986783027649},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38690754771232605},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38263651728630066},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.33771294355392456},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.2102646827697754},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1990114152431488}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.699305534362793},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6526038646697998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6200472712516785},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.5350348353385925},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49805760383605957},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.42602983117103577},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4183986783027649},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38690754771232605},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38263651728630066},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.33771294355392456},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.2102646827697754},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1990114152431488},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/rs13081523","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13081523","pdf_url":"https://www.mdpi.com/2072-4292/13/8/1523/pdf?version=1618740275","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:b95ef8c8527348b297698a782341dda1","is_oa":true,"landing_page_url":"https://doaj.org/article/b95ef8c8527348b297698a782341dda1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"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 13, Iss 8, p 1523 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/8/1523/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13081523","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","raw_type":"Text"},{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/103115","is_oa":true,"landing_page_url":"http://hdl.handle.net/10919/103115","pdf_url":null,"source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"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":null,"raw_type":"Article - Refereed"}],"best_oa_location":{"id":"doi:10.3390/rs13081523","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13081523","pdf_url":"https://www.mdpi.com/2072-4292/13/8/1523/pdf?version=1618740275","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":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8299999833106995}],"awards":[{"id":"https://openalex.org/G7759698952","display_name":null,"funder_award_id":"NNH16ZDA001N-LCLUC","funder_id":"https://openalex.org/F4320306101","funder_display_name":"National Aeronautics and Space Administration"}],"funders":[{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3153636292.pdf","grobid_xml":"https://content.openalex.org/works/W3153636292.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W21834168","https://openalex.org/W29643851","https://openalex.org/W186953100","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1909515874","https://openalex.org/W1958291604","https://openalex.org/W1994668970","https://openalex.org/W1996663388","https://openalex.org/W2004925383","https://openalex.org/W2027000042","https://openalex.org/W2040206062","https://openalex.org/W2041387707","https://openalex.org/W2085665642","https://openalex.org/W2094455438","https://openalex.org/W2101051003","https://openalex.org/W2107880390","https://openalex.org/W2112614772","https://openalex.org/W2123822317","https://openalex.org/W2163605009","https://openalex.org/W2164976328","https://openalex.org/W2464708700","https://openalex.org/W2469938794","https://openalex.org/W2470118601","https://openalex.org/W2533566148","https://openalex.org/W2618530766","https://openalex.org/W2760340275","https://openalex.org/W2907663452","https://openalex.org/W2921499963","https://openalex.org/W2940726923","https://openalex.org/W2955477134","https://openalex.org/W2963173190","https://openalex.org/W2990265654","https://openalex.org/W2991201922","https://openalex.org/W2996836954","https://openalex.org/W3083758784","https://openalex.org/W6607620158","https://openalex.org/W6684191040","https://openalex.org/W6765492952"],"related_works":["https://openalex.org/W3135697610","https://openalex.org/W4375867731","https://openalex.org/W4391621807","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W2088899772"],"abstract_inverted_index":{"High-spatial-resolution":[0],"satellite":[1],"imagery":[2,65],"has":[3],"been":[4],"widely":[5],"applied":[6,70],"for":[7,32,129,156,223,249,252],"detailed":[8],"urban":[9,34,78,84,253],"mapping.":[10,35],"Recently,":[11],"deep":[12],"convolutional":[13],"neural":[14],"networks":[15],"(DCNNs)":[16],"have":[17],"shown":[18],"promise":[19,248],"in":[20,107],"certain":[21],"remote":[22,265],"sensing":[23,266],"applications,":[24],"but":[25],"they":[26],"are":[27],"still":[28],"relatively":[29],"new":[30],"techniques":[31],"general":[33],"This":[36],"study":[37,245],"examines":[38],"the":[39,97,112,157,166,196,208],"use":[40],"of":[41,56,114,200,241,263],"two":[42],"DCNNs":[43],"(U-Net":[44],"and":[45,60,92,179,202,211,238,255],"VGG16)":[46],"to":[47,52,75,95,127,135,151,194,260],"provide":[48],"an":[49,71,171],"automatic":[50],"schema":[51],"support":[53],"high-resolution":[54],"mapping":[55,118,227,254],"buildings,":[57],"road/open":[58],"built-up,":[59],"vegetation":[61],"cover.":[62],"Using":[63],"WorldView-2":[64],"as":[66,190],"input,":[67],"we":[68,103,169],"first":[69],"established":[72],"OBIA":[73],"method":[74],"characterize":[76],"major":[77],"land":[79],"cover":[80],"classes.":[81],"An":[82],"OBIA-derived":[83],"map":[85],"was":[86,141,188],"then":[87],"divided":[88],"into":[89],"a":[90,191,218,261],"training":[91,130,242],"testing":[93,158],"region":[94],"evaluate":[96],"DCNNs\u2019":[98],"performance.":[99],"For":[100,165],"U-Net":[101,120],"mapping,":[102,168],"were":[104],"particularly":[105],"interested":[106],"how":[108],"sample":[109,131],"size":[110],"or":[111],"number":[113,262],"image":[115,137],"tiles":[116,138],"affect":[117],"accuracy.":[119],"generated":[121,217],"cross-validation":[122],"accuracies":[123],"ranging":[124],"from":[125,133],"40.5":[126],"95.2%":[128],"sizes":[132],"32":[134],"4096":[136],"(each":[139],"tile":[140],"256":[142,144],"by":[143],"pixels).":[145],"A":[146],"per-pixel":[147],"accuracy":[148,155,222,228],"assessment":[149],"led":[150],"87.8":[152],"percent":[153,220],"overall":[154,221],"region,":[159],"suggesting":[160],"U-Net\u2019s":[161],"good":[162],"generalization":[163],"capabilities.":[164],"VGG16":[167,210],"proposed":[170],"object-based":[172],"framing":[173],"paradigm":[174],"that":[175],"retains":[176],"spatial":[177],"information":[178],"assists":[180],"machine":[181],"perception":[182],"through":[183],"Gaussian":[184,186],"blurring.":[185],"blurring":[187],"used":[189],"pre-processing":[192],"step":[193],"enhance":[195],"contrast":[197],"between":[198],"objects":[199],"interest":[201],"background":[203],"(contextual)":[204],"information.":[205],"Combined":[206],"with":[207],"pre-trained":[209],"transfer":[212,259],"learning,":[213],"this":[214],"analytical":[215],"approach":[216,257],"77.3":[219],"per-object":[224],"assessment.":[225],"The":[226],"could":[229],"be":[230],"further":[231],"improved":[232],"given":[233],"more":[234],"robust":[235],"segmentation":[236],"algorithms":[237],"better":[239],"quantity/quality":[240],"samples.":[243],"Our":[244],"shows":[246],"significant":[247],"DCNN":[250],"implementation":[251],"our":[256],"can":[258],"other":[264],"applications.":[267]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}