{"id":"https://openalex.org/W2945272354","doi":"https://doi.org/10.3390/rs11101202","title":"A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery","display_name":"A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery","publication_year":2019,"publication_date":"2019-05-21","ids":{"openalex":"https://openalex.org/W2945272354","doi":"https://doi.org/10.3390/rs11101202","mag":"2945272354"},"language":"en","primary_location":{"id":"doi:10.3390/rs11101202","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11101202","pdf_url":"https://www.mdpi.com/2072-4292/11/10/1202/pdf?version=1558411178","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/2072-4292/11/10/1202/pdf?version=1558411178","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119206298","display_name":"Min Ji","orcid":"https://orcid.org/0000-0002-5898-9910"},"institutions":[{"id":"https://openalex.org/I78650965","display_name":"TU Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Min Ji","raw_affiliation_strings":["Institute for Cartography, TU Dresden, 01062 Dresden, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Cartography, TU Dresden, 01062 Dresden, Germany","institution_ids":["https://openalex.org/I78650965"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049165867","display_name":"Lanfa Liu","orcid":"https://orcid.org/0000-0002-2001-7542"},"institutions":[{"id":"https://openalex.org/I78650965","display_name":"TU Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Lanfa Liu","raw_affiliation_strings":["Institute for Cartography, TU Dresden, 01062 Dresden, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Cartography, TU Dresden, 01062 Dresden, Germany","institution_ids":["https://openalex.org/I78650965"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013586960","display_name":"Runlin Du","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116268","display_name":"Qingdao Institute of Marine Geology","ror":"https://ror.org/02aybg366","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2799486974","https://openalex.org/I4210116268"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runlin Du","raw_affiliation_strings":["Qingdao Institute of Marine Geology, Qingdao 266071, China"],"affiliations":[{"raw_affiliation_string":"Qingdao Institute of Marine Geology, Qingdao 266071, China","institution_ids":["https://openalex.org/I4210116268"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013701885","display_name":"Manfred F. Buchroithner","orcid":"https://orcid.org/0000-0002-6051-2249"},"institutions":[{"id":"https://openalex.org/I78650965","display_name":"TU Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Manfred F. Buchroithner","raw_affiliation_strings":["Institute for Cartography, TU Dresden, 01062 Dresden, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Cartography, TU Dresden, 01062 Dresden, Germany","institution_ids":["https://openalex.org/I78650965"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5049165867"],"corresponding_institution_ids":["https://openalex.org/I78650965"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":547,"currency":"EUR","value_usd":589},"fwci":8.7488,"has_fulltext":false,"cited_by_count":102,"citation_normalized_percentile":{"value":0.97893461,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"11","issue":"10","first_page":"1202","last_page":"1202"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9764999747276306,"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/T12424","display_name":"Earthquake Detection and Analysis","score":0.9639000296592712,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.8341455459594727},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7806590795516968},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6848227381706238},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.57074373960495},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.550284743309021},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5094783902168274},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47862687706947327},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.47728458046913147},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4590761363506317},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.424015074968338},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.4208138883113861},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.41141200065612793},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1737360954284668},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17017683386802673},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16018813848495483}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8341455459594727},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7806590795516968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6848227381706238},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.57074373960495},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.550284743309021},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5094783902168274},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47862687706947327},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.47728458046913147},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4590761363506317},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.424015074968338},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.4208138883113861},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.41141200065612793},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1737360954284668},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17017683386802673},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16018813848495483},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs11101202","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11101202","pdf_url":"https://www.mdpi.com/2072-4292/11/10/1202/pdf?version=1558411178","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:16fbd92feb5e4ea0aeb5f3b59b13f351","is_oa":true,"landing_page_url":"https://doaj.org/article/16fbd92feb5e4ea0aeb5f3b59b13f351","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 11, Iss 10, p 1202 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/11/10/1202/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs11101202","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 11; Issue 10; Pages: 1202","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs11101202","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11101202","pdf_url":"https://www.mdpi.com/2072-4292/11/10/1202/pdf?version=1558411178","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":0.6299999952316284}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2945272354.pdf","grobid_xml":"https://content.openalex.org/works/W2945272354.grobid-xml"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W763203301","https://openalex.org/W1528606029","https://openalex.org/W1536340909","https://openalex.org/W1538131130","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1997631708","https://openalex.org/W2002264765","https://openalex.org/W2014907398","https://openalex.org/W2020255423","https://openalex.org/W2023501853","https://openalex.org/W2042204882","https://openalex.org/W2059432853","https://openalex.org/W2073873648","https://openalex.org/W2092730582","https://openalex.org/W2097117768","https://openalex.org/W2098676252","https://openalex.org/W2104896032","https://openalex.org/W2112739286","https://openalex.org/W2130578993","https://openalex.org/W2136884891","https://openalex.org/W2154166506","https://openalex.org/W2163605009","https://openalex.org/W2168048106","https://openalex.org/W2187089797","https://openalex.org/W2222447337","https://openalex.org/W2261059368","https://openalex.org/W2285827343","https://openalex.org/W2286118103","https://openalex.org/W2333566963","https://openalex.org/W2343702461","https://openalex.org/W2346062110","https://openalex.org/W2415636451","https://openalex.org/W2461549388","https://openalex.org/W2512520488","https://openalex.org/W2533566148","https://openalex.org/W2545803144","https://openalex.org/W2547102124","https://openalex.org/W2593771152","https://openalex.org/W2601497269","https://openalex.org/W2601726217","https://openalex.org/W2618530766","https://openalex.org/W2621663019","https://openalex.org/W2751694392","https://openalex.org/W2764034829","https://openalex.org/W2766447205","https://openalex.org/W2771195237","https://openalex.org/W2781781489","https://openalex.org/W2782522152","https://openalex.org/W2793091350","https://openalex.org/W2811197153","https://openalex.org/W2883466786","https://openalex.org/W2891747104","https://openalex.org/W2898300628","https://openalex.org/W2911964244","https://openalex.org/W2919115771","https://openalex.org/W3102619772","https://openalex.org/W3104341624","https://openalex.org/W4232345992","https://openalex.org/W6746583464","https://openalex.org/W6753035760"],"related_works":["https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3193043704","https://openalex.org/W3171520305","https://openalex.org/W1924178503","https://openalex.org/W4308716060","https://openalex.org/W4280648719","https://openalex.org/W3135126032","https://openalex.org/W3033346322","https://openalex.org/W2889302474"],"abstract_inverted_index":{"The":[0,123,186,220],"accurate":[1],"and":[2,38,66,81,113,143,177,211],"quick":[3],"derivation":[4],"of":[5,8,22,61,141,147],"the":[6,16,20,53,56,59,76,96,120,127,135,151,163,179,202,212,224],"distribution":[7],"damaged":[9],"building":[10,35,50],"must":[11],"be":[12],"considered":[13,90],"essential":[14],"for":[15,33,157,188,232],"emergency":[17],"response.":[18],"With":[19],"success":[21],"deep":[23,155],"learning,":[24],"there":[25],"is":[26],"an":[27,139],"increasing":[28,171],"interest":[29],"to":[30,91,153,162,175,184,209,218],"apply":[31],"it":[32],"earthquake-induced":[34],"damage":[36,51],"mapping,":[37],"its":[39],"performance":[40,60],"has":[41],"not":[42],"been":[43],"compared":[44,161,200],"with":[45,75,130,166,197,201],"conventional":[46],"methods":[47],"in":[48],"detecting":[49],"after":[52,95],"earthquake.":[54,99],"In":[55],"present":[57],"study,":[58],"grey-level":[62],"co-occurrence":[63],"matrix":[64],"texture":[65,164,230],"convolutional":[67],"neural":[68],"network":[69],"(CNN)":[70],"features":[71,156,196,227,231],"were":[72,89,117],"comparatively":[73],"evaluated":[74],"random":[77,131,167,198],"forest":[78,132,168,199],"classifier.":[79],"Pre-":[80],"post-event":[82],"very":[83],"high-resolution":[84],"(VHR)":[85],"remote":[86],"sensing":[87],"imagery":[88],"identify":[92],"collapsed":[93,159,234],"buildings":[94,160,190,235],"2010":[97],"Haiti":[98],"Overall":[100],"accuracy":[101,111,115,187],"(OA),":[102],"allocation":[103],"disagreement":[104,107,146,181,214],"(AD),":[105],"quantity":[106],"(QD),":[108],"Kappa,":[109],"user":[110],"(UA),":[112],"producer":[114],"(PA)":[116],"used":[118],"as":[119],"evaluation":[121],"metrics.":[122],"results":[124,221],"showed":[125],"that":[126,223],"CNN":[128,195,203,226],"feature":[129,165],"method":[133,169],"had":[134],"best":[136],"performance,":[137],"achieving":[138],"OA":[140,205],"87.6%":[142],"a":[144],"total":[145,180,213],"12.4%.":[148,219],"CNNs":[149],"have":[150],"potential":[152],"extract":[154],"identifying":[158,189,233],"by":[170,193],"Kappa":[172],"from":[173,182,207,216],"61.7%":[174],"69.5%":[176],"reducing":[178],"16.6%":[183],"14.1%.":[185],"was":[191],"improved":[192],"combining":[194],"approach.":[204],"increased":[206],"85.9%":[208],"87.6%,":[210],"reduced":[215],"14.1%":[217],"indicate":[222],"learnt":[225],"can":[228],"outperform":[229],"using":[236],"VHR":[237],"remotely":[238],"sensed":[239],"space":[240],"imagery.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}