{"id":"https://openalex.org/W3092214930","doi":"https://doi.org/10.3390/ijgi9100601","title":"Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets","display_name":"Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets","publication_year":2020,"publication_date":"2020-10-12","ids":{"openalex":"https://openalex.org/W3092214930","doi":"https://doi.org/10.3390/ijgi9100601","mag":"3092214930"},"language":"en","primary_location":{"id":"doi:10.3390/ijgi9100601","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi9100601","pdf_url":"https://www.mdpi.com/2220-9964/9/10/601/pdf","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"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":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2220-9964/9/10/601/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076998023","display_name":"Ahram Song","orcid":"https://orcid.org/0000-0002-9190-2848"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ahram Song","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea"],"raw_orcid":"https://orcid.org/0000-0002-9190-2848","affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101964120","display_name":"Yongil Kim","orcid":"https://orcid.org/0000-0003-0541-8986"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yongil Kim","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101964120"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":{"value":1700,"currency":"CHF","value_usd":1893},"apc_paid":{"value":1700,"currency":"CHF","value_usd":1893},"fwci":1.5416,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.83486744,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"9","issue":"10","first_page":"601","last_page":"601"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9990000128746033,"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":0.9976999759674072,"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":0.7693364024162292},{"id":"https://openalex.org/keywords/cityscape","display_name":"Cityscape","score":0.7498925924301147},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6524630784988403},{"id":"https://openalex.org/keywords/photogrammetry","display_name":"Photogrammetry","score":0.5275454521179199},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5217500925064087},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.5166606903076172},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4929661750793457},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49000683426856995},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4339432716369629},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.41962242126464844},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.38703039288520813},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35672587156295776},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1123245358467102}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7693364024162292},{"id":"https://openalex.org/C2779685930","wikidata":"https://www.wikidata.org/wiki/Q1935974","display_name":"Cityscape","level":2,"score":0.7498925924301147},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6524630784988403},{"id":"https://openalex.org/C117455697","wikidata":"https://www.wikidata.org/wiki/Q190149","display_name":"Photogrammetry","level":2,"score":0.5275454521179199},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5217500925064087},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.5166606903076172},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4929661750793457},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49000683426856995},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4339432716369629},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.41962242126464844},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.38703039288520813},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35672587156295776},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1123245358467102},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/ijgi9100601","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi9100601","pdf_url":"https://www.mdpi.com/2220-9964/9/10/601/pdf","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"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":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:20eb07496b7541cc95d7fe08d01c7a75","is_oa":true,"landing_page_url":"https://doaj.org/article/20eb07496b7541cc95d7fe08d01c7a75","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":"ISPRS International Journal of Geo-Information, Vol 9, Iss 10, p 601 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2220-9964/9/10/601/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/ijgi9100601","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":"ISPRS International Journal of Geo-Information; Volume 9; Issue 10; Pages: 601","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/ijgi9100601","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi9100601","pdf_url":"https://www.mdpi.com/2220-9964/9/10/601/pdf","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"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":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7200000286102295,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G1251031942","display_name":null,"funder_award_id":"BK 21 four","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G1798811926","display_name":null,"funder_award_id":"2019R1I1A2A01058144","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G2203846861","display_name":null,"funder_award_id":"the BK21 FOUR","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G30685149","display_name":null,"funder_award_id":"BK21 FOUR","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3092214930.pdf","grobid_xml":"https://content.openalex.org/works/W3092214930.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1937812750","https://openalex.org/W2002427601","https://openalex.org/W2037227137","https://openalex.org/W2102673432","https://openalex.org/W2108598243","https://openalex.org/W2248723555","https://openalex.org/W2272479860","https://openalex.org/W2340897893","https://openalex.org/W2395611524","https://openalex.org/W2527276685","https://openalex.org/W2616755213","https://openalex.org/W2732412926","https://openalex.org/W2735768354","https://openalex.org/W2752788177","https://openalex.org/W2760340275","https://openalex.org/W2793268137","https://openalex.org/W2799406003","https://openalex.org/W2804532080","https://openalex.org/W2897722020","https://openalex.org/W2902190145","https://openalex.org/W2904122576","https://openalex.org/W2915971115","https://openalex.org/W2940249560","https://openalex.org/W2946707925","https://openalex.org/W2962679318","https://openalex.org/W2963787264","https://openalex.org/W2963995737","https://openalex.org/W2992869700","https://openalex.org/W4253153980","https://openalex.org/W6652571466","https://openalex.org/W6741354212"],"related_works":["https://openalex.org/W1974241303","https://openalex.org/W2365698185","https://openalex.org/W2278970506","https://openalex.org/W2383527859","https://openalex.org/W2066186317","https://openalex.org/W2348479303","https://openalex.org/W2382644551","https://openalex.org/W2605894374","https://openalex.org/W2386046054","https://openalex.org/W2372478594"],"abstract_inverted_index":{"Although":[0],"semantic":[1,146],"segmentation":[2,147,185,236],"of":[3,39,69,128,149,181,187,219,238,248],"remote-sensing":[4],"(RS)":[5],"images":[6,21,190,240],"using":[7,55,154,163,249],"deep-learning":[8,40],"networks":[9],"has":[10],"demonstrated":[11],"its":[12],"effectiveness":[13],"recently,":[14],"compared":[15],"with":[16,84,170,245],"natural-image":[17,117],"datasets,":[18,87,118],"obtaining":[19],"RS":[20,115],"under":[22],"the":[23,36,79,85,89,98,114,121,129,145,150,165,178,184,188,194,211,217,227],"same":[24],"conditions":[25],"to":[26,235,243],"construct":[27],"data":[28,132,152,167,214],"labels":[29],"is":[30,53,158,191,198,224],"difficult.":[31],"Indeed,":[32],"small":[33],"datasets":[34,110,142,208],"limit":[35],"effective":[37],"learning":[38],"networks.":[41],"To":[42],"address":[43],"this":[44],"problem,":[45],"we":[46],"propose":[47],"a":[48,56],"combined":[49,57,201],"U-net":[50,202],"model":[51,203],"that":[52,77,138,161,226],"trained":[54],"weighted":[58],"loss":[59],"function":[60],"and":[61,71,88,103,108,116,177,209],"can":[62,204,230],"handle":[63],"heterogeneous":[64,86,141,207,251],"datasets.":[65,221,252],"The":[66,74,200],"network":[67],"consists":[68],"encoder":[70,80],"decoder":[72,90],"blocks.":[73],"convolutional":[75],"layers":[76,122],"form":[78],"blocks":[81,91],"are":[82,92,111,123,133,143],"shared":[83],"assigned":[93],"separate":[94],"training":[95,213],"weights.":[96],"Herein,":[97],"International":[99],"Society":[100],"for":[101],"Photogrammetry":[102],"Remote":[104],"Sensing":[105],"(ISPRS)":[106],"Potsdam":[107,131,151,166,189],"Cityscape":[109,196],"used":[112],"as":[113,174],"respectively.":[119],"When":[120],"shared,":[124],"only":[125,164,232],"visible":[126],"bands":[127],"ISPRS":[130],"used.":[134,199],"Experimental":[135],"results":[136],"show":[137],"when":[139,193],"same-sized":[140],"used,":[144],"accuracy":[148,186],"obtained":[153,162],"our":[155],"proposed":[156,228],"method":[157,229],"lower":[159],"than":[160],"(four":[168],"bands)":[169],"other":[171],"methods,":[172],"such":[173],"SegNet,":[175],"DeepLab-V3+,":[176],"simplified":[179],"version":[180],"U-net.":[182],"However,":[183],"improved":[192],"larger":[195],"dataset":[197],"effectively":[205],"train":[206],"overcome":[210],"insufficient":[212],"problem":[215],"in":[216],"context":[218],"RS-image":[220],"Furthermore,":[222],"it":[223],"expected":[225],"not":[231],"be":[233],"applied":[234],"tasks":[237,244],"aerial":[239],"but":[241],"also":[242],"various":[246],"purposes":[247],"big":[250]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":3}],"updated_date":"2026-08-02T14:50:37.381335","created_date":"2025-10-10T00:00:00"}
