{"id":"https://openalex.org/W4213068057","doi":"https://doi.org/10.3390/rs14041002","title":"Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake","display_name":"Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4213068057","doi":"https://doi.org/10.3390/rs14041002"},"language":"en","primary_location":{"id":"doi:10.3390/rs14041002","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14041002","pdf_url":"https://www.mdpi.com/2072-4292/14/4/1002/pdf?version=1645438421","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/2072-4292/14/4/1002/pdf?version=1645438421","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055196482","display_name":"Yihao Zhan","orcid":null},"institutions":[{"id":"https://openalex.org/I159385669","display_name":"Chiba University","ror":"https://ror.org/01hjzeq58","country_code":"JP","type":"education","lineage":["https://openalex.org/I159385669"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yihao Zhan","raw_affiliation_strings":["Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan","institution_ids":["https://openalex.org/I159385669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017385131","display_name":"Wen Liu","orcid":"https://orcid.org/0000-0002-0655-4114"},"institutions":[{"id":"https://openalex.org/I159385669","display_name":"Chiba University","ror":"https://ror.org/01hjzeq58","country_code":"JP","type":"education","lineage":["https://openalex.org/I159385669"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Wen Liu","raw_affiliation_strings":["Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan","Research Institute of Disaster Medicine, Chiba University, Chiba 263-8522, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan","institution_ids":["https://openalex.org/I159385669"]},{"raw_affiliation_string":"Research Institute of Disaster Medicine, Chiba University, Chiba 263-8522, Japan","institution_ids":["https://openalex.org/I159385669"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074219449","display_name":"Yoshihisa Maruyama","orcid":"https://orcid.org/0000-0001-8320-7207"},"institutions":[{"id":"https://openalex.org/I159385669","display_name":"Chiba University","ror":"https://ror.org/01hjzeq58","country_code":"JP","type":"education","lineage":["https://openalex.org/I159385669"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshihisa Maruyama","raw_affiliation_strings":["Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan","institution_ids":["https://openalex.org/I159385669"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055196482"],"corresponding_institution_ids":["https://openalex.org/I159385669"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":5.0851,"has_fulltext":true,"cited_by_count":50,"citation_normalized_percentile":{"value":0.95973109,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"14","issue":"4","first_page":"1002","last_page":"1002"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9973000288009644,"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":0.9973000288009644,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.9182000160217285,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer 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":0.5831933617591858},{"id":"https://openalex.org/keywords/typhoon","display_name":"Typhoon","score":0.5420430898666382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5188432931900024},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.47284772992134094},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43775731325149536},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4322258234024048},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4256436824798584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35779279470443726},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3059852421283722},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.1888626217842102},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.16842171549797058},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09968671202659607}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5831933617591858},{"id":"https://openalex.org/C181654704","wikidata":"https://www.wikidata.org/wiki/Q140588","display_name":"Typhoon","level":2,"score":0.5420430898666382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5188432931900024},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.47284772992134094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43775731325149536},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4322258234024048},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4256436824798584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35779279470443726},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3059852421283722},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.1888626217842102},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.16842171549797058},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09968671202659607},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14041002","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14041002","pdf_url":"https://www.mdpi.com/2072-4292/14/4/1002/pdf?version=1645438421","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:e91bc50b648847b89ad8f54487427866","is_oa":true,"landing_page_url":"https://doaj.org/article/e91bc50b648847b89ad8f54487427866","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 4, p 1002 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/4/1002/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14041002","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 14; Issue 4; Pages: 1002","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14041002","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14041002","pdf_url":"https://www.mdpi.com/2072-4292/14/4/1002/pdf?version=1645438421","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":[{"display_name":"Sustainable cities and communities","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/11"},{"display_name":"Climate action","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G12401395","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320320912","funder_display_name":"Ministry of Education, Culture, Sports, Science and Technology"},{"id":"https://openalex.org/G7693551792","display_name":null,"funder_award_id":"(MEXT)","funder_id":"https://openalex.org/F4320320912","funder_display_name":"Ministry of Education, Culture, Sports, Science and Technology"},{"id":"https://openalex.org/G8044579487","display_name":null,"funder_award_id":"Japan","funder_id":"https://openalex.org/F4320320912","funder_display_name":"Ministry of Education, Culture, Sports, Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320320912","display_name":"Ministry of Education, Culture, Sports, Science and Technology","ror":"https://ror.org/048rj2z13"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4213068057.pdf","grobid_xml":"https://content.openalex.org/works/W4213068057.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W1903029394","https://openalex.org/W1966716734","https://openalex.org/W1971444184","https://openalex.org/W1989933653","https://openalex.org/W2082958922","https://openalex.org/W2089660146","https://openalex.org/W2102605133","https://openalex.org/W2125555529","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2333566963","https://openalex.org/W2565428746","https://openalex.org/W2565639579","https://openalex.org/W2584136074","https://openalex.org/W2585684434","https://openalex.org/W2597883269","https://openalex.org/W2612624696","https://openalex.org/W2808387360","https://openalex.org/W2809850198","https://openalex.org/W2893124029","https://openalex.org/W2901712839","https://openalex.org/W2927122915","https://openalex.org/W2939781831","https://openalex.org/W2963150697","https://openalex.org/W2963516811","https://openalex.org/W2963718163","https://openalex.org/W2963857746","https://openalex.org/W2969291040","https://openalex.org/W2988812296","https://openalex.org/W3000509019","https://openalex.org/W3007391486","https://openalex.org/W3022498288","https://openalex.org/W3038091703","https://openalex.org/W3085509852","https://openalex.org/W3112139896","https://openalex.org/W3135903521","https://openalex.org/W3196242262","https://openalex.org/W3199228164","https://openalex.org/W6647539285","https://openalex.org/W6718670374"],"related_works":["https://openalex.org/W2322294131","https://openalex.org/W4377023816","https://openalex.org/W2044728225","https://openalex.org/W2381913349","https://openalex.org/W3125308484","https://openalex.org/W2368202014","https://openalex.org/W2001663785","https://openalex.org/W1526491517","https://openalex.org/W2787131064","https://openalex.org/W2903862324"],"abstract_inverted_index":{"Remote":[0],"sensing":[1],"is":[2],"an":[3,17,106],"effective":[4],"method":[5],"of":[6,28,49,70,179],"evaluating":[7],"building":[8,170],"damage":[9,39,59,95,190],"after":[10,140],"a":[11,20,47,79,117],"large-scale":[12],"natural":[13],"disaster,":[14],"such":[15],"as":[16,151],"earthquake":[18],"or":[19],"typhoon.":[21],"In":[22,44,76],"recent":[23],"years,":[24],"with":[25,158],"the":[26,52,61,71,141,152,159,177,188],"development":[27],"computer":[29],"vision":[30],"technology,":[31],"deep":[32,80],"learning":[33,81],"algorithms":[34],"have":[35],"been":[36],"used":[37,150],"for":[38,169,176,187],"assessment":[40],"from":[41,97],"aerial":[42,99,131],"images.":[43,100],"April":[45,136],"2016,":[46],"series":[48],"earthquakes":[50],"hit":[51],"Kyushu":[53],"region,":[54],"Japan,":[55],"and":[56,63,73,92,111,154,172],"caused":[57],"severe":[58],"in":[60,144],"Kumamoto":[62,147],"Oita":[64],"Prefectures.":[65],"Numerous":[66],"buildings":[67,91],"collapsed":[68],"because":[69],"strong":[72],"continuous":[74],"shaking.":[75],"this":[77],"study,":[78],"model":[82,104,164],"called":[83],"Mask":[84,102],"R-CNN":[85,103],"was":[86,124,192],"modified":[87],"to":[88,127],"extract":[89],"residential":[90],"estimate":[93],"their":[94],"levels":[96],"post-event":[98],"Our":[101],"employs":[105],"improved":[107],"feature":[108],"pyramid":[109],"network":[110],"online":[112],"hard":[113],"example":[114],"mining.":[115],"Furthermore,":[116],"non-maximum":[118],"suppression":[119],"algorithm":[120],"across":[121],"multiple":[122],"classes":[123,191],"also":[125],"applied":[126],"improve":[128],"prediction.":[129],"The":[130,183],"images":[132],"captured":[133],"on":[134],"29":[135],"2016":[137],"(two":[138],"weeks":[139],"main":[142],"shock)":[143],"Mashiki":[145],"Town,":[146],"Prefecture,":[148],"were":[149],"training":[153],"test":[155],"sets.":[156],"Compared":[157],"field":[160],"survey":[161],"results,":[162],"our":[163],"achieved":[165],"approximately":[166,193],"95%":[167],"accuracy":[168,175,186],"extraction":[171],"over":[173],"92%":[174],"detection":[178],"severely":[180],"damaged":[181],"buildings.":[182],"overall":[184],"classification":[185],"four":[189],"88%,":[194],"demonstrating":[195],"acceptable":[196],"performance.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":3}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2022-02-24T00:00:00"}