{"id":"https://openalex.org/W4387829580","doi":"https://doi.org/10.1109/igarss52108.2023.10282031","title":"Machine Learning-Based Approach For Landslide Susceptibility Mapping Using Multimodal Data","display_name":"Machine Learning-Based Approach For Landslide Susceptibility Mapping Using Multimodal Data","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387829580","doi":"https://doi.org/10.1109/igarss52108.2023.10282031"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10282031","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10282031","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5055382963","display_name":"Xianping Ma","orcid":"https://orcid.org/0000-0002-2180-2964"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianping Ma","raw_affiliation_strings":["The Chinese University of Hong Kong,School of Science and Engineering,Shenzhen,China","School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,School of Science and Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I4210116924"]},{"raw_affiliation_string":"School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040559125","display_name":"Man-On Pun","orcid":"https://orcid.org/0000-0003-3316-5381"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Man-On Pun","raw_affiliation_strings":["The Chinese University of Hong Kong,School of Science and Engineering,Shenzhen,China","School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,School of Science and Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I4210116924"]},{"raw_affiliation_string":"School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082969851","display_name":"Ming Liu","orcid":"https://orcid.org/0000-0002-2160-6111"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Liu","raw_affiliation_strings":["MizarVision,Shanghai,China","MizarVision, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MizarVision,Shanghai,China","institution_ids":[]},{"raw_affiliation_string":"MizarVision, Shanghai, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003629926","display_name":"Yang Wang","orcid":"https://orcid.org/0000-0003-4885-4550"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Wang","raw_affiliation_strings":["Xi’an Leitong Technology Co. Ltd,Xi’an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Leitong Technology Co. Ltd,Xi’an,China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5174","last_page":"5177"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10535","display_name":"Landslides and related hazards","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T10535","display_name":"Landslides and related hazards","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T10555","display_name":"Fire effects on ecosystems","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T10644","display_name":"Cryospheric studies and observations","score":0.979200005531311,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/landslide","display_name":"Landslide","score":0.8177282810211182},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.584442138671875},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5193715691566467},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4795607924461365},{"id":"https://openalex.org/keywords/debris","display_name":"Debris","score":0.47937753796577454},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46899914741516113},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.46427199244499207},{"id":"https://openalex.org/keywords/debris-flow","display_name":"Debris flow","score":0.462583065032959},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.4285374581813812},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4222966432571411},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4138743281364441},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.41138264536857605},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3733792304992676},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2821108102798462},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.23074215650558472},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12464606761932373},{"id":"https://openalex.org/keywords/geomorphology","display_name":"Geomorphology","score":0.10471224784851074}],"concepts":[{"id":"https://openalex.org/C186295008","wikidata":"https://www.wikidata.org/wiki/Q167903","display_name":"Landslide","level":2,"score":0.8177282810211182},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.584442138671875},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5193715691566467},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4795607924461365},{"id":"https://openalex.org/C2776023875","wikidata":"https://www.wikidata.org/wiki/Q637703","display_name":"Debris","level":2,"score":0.47937753796577454},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46899914741516113},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.46427199244499207},{"id":"https://openalex.org/C2776643431","wikidata":"https://www.wikidata.org/wiki/Q1817963","display_name":"Debris flow","level":3,"score":0.462583065032959},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.4285374581813812},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4222966432571411},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4138743281364441},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.41138264536857605},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3733792304992676},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2821108102798462},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.23074215650558472},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12464606761932373},{"id":"https://openalex.org/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.10471224784851074},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10282031","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10282031","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1870144835","https://openalex.org/W2023203753","https://openalex.org/W2117350110","https://openalex.org/W2280221537"],"related_works":["https://openalex.org/W2911563142","https://openalex.org/W187473655","https://openalex.org/W2991434004","https://openalex.org/W2093486058","https://openalex.org/W2793141244","https://openalex.org/W1636843286","https://openalex.org/W2356868884","https://openalex.org/W2357620750","https://openalex.org/W2356402003","https://openalex.org/W2382573398"],"abstract_inverted_index":{"In":[0,22],"this":[1],"work,":[2],"a":[3,30,68,90],"practical":[4],"Machine":[5],"Learning":[6],"(ML)":[7],"approach":[8],"is":[9,124],"proposed":[10,36,151],"to":[11,24,58,88,126,156],"produce":[12],"Landslide":[13,46],"Susceptibility":[14],"Mapping":[15],"(LSM)":[16],"by":[17,93,119],"exploiting":[18],"multiple":[19],"data":[20,53,104],"sources.":[21],"contrast":[23],"conventional":[25],"ML-based":[26],"methods":[27],"that":[28,149],"consider":[29],"large":[31],"number":[32],"of":[33,96,115,130,133],"factors,":[34,43],"the":[35,107,113,128,131,150],"method":[37],"fuses":[38],"only":[39],"three":[40],"carefully":[41],"selected":[42],"namely":[44],"inventory":[45],"and":[47,54,60,63,80,135,142],"Debris":[48],"Flow":[49],"(LDF)":[50],"data,":[51],"rainfall":[52,102],"forest":[55,98],"change":[56],"information":[57],"model":[59],"predict":[61,127],"landslides":[62],"debris":[64,136,143],"flow.":[65,137],"More":[66],"specifically,":[67],"Global":[69],"Deforestation":[70],"Detection":[71],"Algorithm":[72],"(GDDA)":[73],"based":[74],"on":[75,101],"Synthetic":[76],"Aperture":[77],"Radar":[78,110],"(SAR)":[79],"Convolutional":[81],"Neural":[82],"Network":[83],"(CNN)":[84],"are":[85],"first":[86],"developed":[87],"generate":[89],"hazard":[91],"map":[92],"identifying":[94],"areas":[95,114],"substantial":[97],"changes.":[99],"Capitalizing":[100],"forecast":[103],"derived":[105],"from":[106],"Doppler":[108],"Weather":[109],"(DWR)":[111],"over":[112],"severe":[116],"deforestation":[117],"detected":[118],"GDDA,":[120],"an":[121],"SVM-based":[122],"classifier":[123],"established":[125],"likelihood":[129],"occurrence":[132],"landslide":[134,141],"Using":[138],"two":[139],"real-world":[140],"flows":[144],"in":[145,160],"2021,":[146],"we":[147],"demonstrate":[148],"LSM":[152],"system":[153],"was":[154],"able":[155],"provide":[157],"early":[158],"warning":[159],"advance.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}