{"id":"https://openalex.org/W2997670443","doi":"https://doi.org/10.1109/dicta47822.2019.8945903","title":"Ensemble of Training Models for Road and Building Segmentation","display_name":"Ensemble of Training Models for Road and Building Segmentation","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W2997670443","doi":"https://doi.org/10.1109/dicta47822.2019.8945903","mag":"2997670443"},"language":"en","primary_location":{"id":"doi:10.1109/dicta47822.2019.8945903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta47822.2019.8945903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","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/A5112289748","display_name":"Ryosuke Kamiya","orcid":null},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryosuke Kamiya","raw_affiliation_strings":["Meijo Univresity, 1-501 Shiogamaguchi, Tempaku-ku, Japan"],"affiliations":[{"raw_affiliation_string":"Meijo Univresity, 1-501 Shiogamaguchi, Tempaku-ku, Japan","institution_ids":["https://openalex.org/I96636082"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025639179","display_name":"Sawada Kyoya","orcid":null},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kyoya Sawada","raw_affiliation_strings":["Meijo Univresity, 1-501 Shiogamaguchi, Tempaku-ku, Japan"],"affiliations":[{"raw_affiliation_string":"Meijo Univresity, 1-501 Shiogamaguchi, Tempaku-ku, Japan","institution_ids":["https://openalex.org/I96636082"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103163418","display_name":"Kazuhiro Hotta","orcid":"https://orcid.org/0000-0002-5675-8713"},"institutions":[{"id":"https://openalex.org/I96636082","display_name":"Meijo University","ror":"https://ror.org/04h42fc75","country_code":"JP","type":"education","lineage":["https://openalex.org/I96636082"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuhiro Hotta","raw_affiliation_strings":["Meijo Univresity, 1-501 Shiogamaguchi, Tempaku-ku, Japan"],"affiliations":[{"raw_affiliation_string":"Meijo Univresity, 1-501 Shiogamaguchi, Tempaku-ku, Japan","institution_ids":["https://openalex.org/I96636082"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112289748"],"corresponding_institution_ids":["https://openalex.org/I96636082"],"apc_list":null,"apc_paid":null,"fwci":0.214,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5851712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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":0.9962999820709229,"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/ensemble-learning","display_name":"Ensemble learning","score":0.7617131471633911},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7242924571037292},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7116859555244446},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.6900067329406738},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6627047657966614},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5932754278182983},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5745947957038879},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5419971942901611},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5388725399971008},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48655763268470764},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4705653190612793},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4581758677959442},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4302974343299866},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13166514039039612},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0643576979637146}],"concepts":[{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7617131471633911},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7242924571037292},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7116859555244446},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.6900067329406738},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6627047657966614},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5932754278182983},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5745947957038879},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5419971942901611},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5388725399971008},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48655763268470764},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4705653190612793},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4581758677959442},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4302974343299866},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13166514039039612},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0643576979637146},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta47822.2019.8945903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta47822.2019.8945903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2124592697","https://openalex.org/W2194775991","https://openalex.org/W2623331213","https://openalex.org/W2740819638","https://openalex.org/W2774320778","https://openalex.org/W2793277020","https://openalex.org/W2963785012","https://openalex.org/W6639824700"],"related_works":["https://openalex.org/W2944292463","https://openalex.org/W3014252901","https://openalex.org/W2188759683","https://openalex.org/W2794896638","https://openalex.org/W4317376680","https://openalex.org/W4360777922","https://openalex.org/W2891633941","https://openalex.org/W3202800081","https://openalex.org/W3101614107","https://openalex.org/W1909207154"],"abstract_inverted_index":{"In":[0,49],"this":[1],"paper,":[2],"we":[3,37,52],"propose":[4],"an":[5],"object":[6],"segmentation":[7,97],"method":[8,117,131],"in":[9,77,121],"satellite":[10],"images":[11],"by":[12,30,108,128],"the":[13,25,41,55,61,87,101,124,137],"ensemble":[14,26,42,88,102,139],"of":[15,27,43,89,100,103,140],"models":[16,28,44,56,105],"obtained":[17,29,45],"through":[18,46,72],"training":[19,47,73,78,104],"process.":[20,48],"To":[21],"improve":[22],"recognition":[23],"accuracy,":[24],"different":[31,58,81],"random":[32],"seeds":[33],"is":[34,93,106],"used.":[35],"Here":[36],"pay":[38],"attention":[39],"to":[40],"model":[50,76,126],"ensemble,":[51],"should":[53],"integrate":[54],"with":[57,63,123],"opinions.":[59],"Since":[60],"pixels":[62],"low":[64],"probability":[65,82,91],"such":[66],"as":[67],"boundary":[68,84],"are":[69],"often":[70],"updated":[71],"process,":[74],"each":[75],"process":[79],"has":[80],"for":[83,95],"regions,":[85],"and":[86,112],"those":[90],"maps":[92],"effective":[94],"improving":[96],"accuracy.":[98],"Effectiveness":[99],"demonstrated":[107],"experiments":[109],"on":[110],"building":[111],"road":[113],"segmentation.":[114],"Our":[115,130],"proposed":[116],"improved":[118],"approximately":[119],"4%":[120],"comparison":[122],"best":[125],"selected":[127],"validation.":[129],"also":[132],"achieved":[133],"better":[134],"accuracy":[135],"than":[136],"standard":[138],"models.":[141]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
