{"id":"https://openalex.org/W3132286737","doi":"https://doi.org/10.1109/igarss39084.2020.9324267","title":"Global Semantic Land Use/Land Cover Based on High Resolution Satellite Imagery Using Ensemble Networks","display_name":"Global Semantic Land Use/Land Cover Based on High Resolution Satellite Imagery Using Ensemble Networks","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3132286737","doi":"https://doi.org/10.1109/igarss39084.2020.9324267","mag":"3132286737"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9324267","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324267","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","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/A5006828466","display_name":"Gustav Tapper","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gustav Tapper","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052408357","display_name":"Carl Sundelius","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carl Sundelius","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061857442","display_name":"Leif Haglund","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leif Haglund","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16479484,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1070","last_page":"1073"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9976999759674072,"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"}},"topics":[{"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.996999979019165,"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":0.996999979019165,"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":0.7353566884994507},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.678240180015564},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.6704163551330566},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6152377724647522},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.5718188881874084},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.545036256313324},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.5406314134597778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5155637264251709},{"id":"https://openalex.org/keywords/panchromatic-film","display_name":"Panchromatic film","score":0.507365882396698},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.48497605323791504},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.483009934425354},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4827105700969696},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.47507303953170776},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4723699688911438},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42864513397216797},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41075676679611206},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3619955778121948},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.27750492095947266},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.181278795003891},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18012255430221558},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12938454747200012}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7353566884994507},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.678240180015564},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.6704163551330566},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6152377724647522},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.5718188881874084},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.545036256313324},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.5406314134597778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5155637264251709},{"id":"https://openalex.org/C107445234","wikidata":"https://www.wikidata.org/wiki/Q280995","display_name":"Panchromatic film","level":3,"score":0.507365882396698},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.48497605323791504},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.483009934425354},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4827105700969696},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.47507303953170776},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4723699688911438},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42864513397216797},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41075676679611206},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3619955778121948},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.27750492095947266},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.181278795003891},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18012255430221558},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12938454747200012},{"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/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace 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":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9324267","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324267","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1490324445","https://openalex.org/W1554663460","https://openalex.org/W2469938794","https://openalex.org/W2563555309","https://openalex.org/W2788939676","https://openalex.org/W3213429344","https://openalex.org/W4388297464"],"related_works":["https://openalex.org/W1930929277","https://openalex.org/W2158394102","https://openalex.org/W2361746014","https://openalex.org/W1502637513","https://openalex.org/W2124952510","https://openalex.org/W2375311607","https://openalex.org/W2022261651","https://openalex.org/W2143372509","https://openalex.org/W2565514930","https://openalex.org/W3007156798"],"abstract_inverted_index":{"This":[0,90],"paper":[1],"describes":[2],"an":[3,106],"architecture":[4,108],"for":[5],"and":[6,24,100],"the":[7,41,44,58],"results":[8,95],"of":[9,43,92,105,111],"global":[10,98],"Land":[11],"Use/Land":[12],"Cover":[13],"semantic":[14,64],"segmentation.":[15],"It":[16],"is":[17],"based":[18],"on":[19],"Vricon":[20,71],"3D":[21,45],"Surface":[22,46],"Models":[23],"high":[25],"resolution,":[26,30],"better":[27],"than":[28],"0.5m":[29],"multispectral":[31],"satellite":[32],"images.":[33],"Vri-con":[34],"uses":[35,72],"deep":[36],"learning":[37],"to":[38,117],"automatically":[39],"classify":[40],"content":[42],"Models.":[47],"Ortho-rectified":[48],"2D-images":[49],"combined":[50],"with":[51,66,83,96],"a":[52,62,73,97],"rasterized":[53],"DSM":[54],"are":[55],"used":[56],"as":[57],"input.":[59],"To":[60],"create":[61],"state-of-the-art":[63],"segmentation":[65],"0.5":[67],"meters":[68],"per":[69],"pixel,":[70],"network":[74],"ensemble":[75,91,107],"containing":[76],"multiple":[77],"Fully":[78],"Convolutional":[79],"Networks":[80],"(FCNs),":[81],"each":[82],"different":[84,112],"inputs,":[85],"outputs,":[86],"or":[87],"training":[88],"data.":[89],"FCNs":[93],"produces":[94],"scale":[99],"excellent":[101],"accuracy.":[102],"The":[103],"use":[104],"enables":[109],"usage":[110],"annotated":[113],"data,":[114],"preventing":[115],"overfitting":[116],"certain":[118],"geographic":[119],"areas.":[120]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}