{"id":"https://openalex.org/W6944347583","doi":"https://doi.org/10.21227/g6nd-zt37","title":"Qinghai-Tibet Plateau (QTP) Landslides Dataset","display_name":"Qinghai-Tibet Plateau (QTP) Landslides Dataset","publication_year":2024,"publication_date":"2024-10-30","ids":{"openalex":"https://openalex.org/W6944347583","doi":"https://doi.org/10.21227/g6nd-zt37"},"language":"en","primary_location":{"id":"doi:10.21227/g6nd-zt37","is_oa":true,"landing_page_url":"https://doi.org/10.21227/g6nd-zt37","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/g6nd-zt37","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Qi, Shengwen","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi, Shengwen","raw_affiliation_strings":["Institute of Geology and Geophysics, Chinese Academy of Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geology and Geophysics, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Zan","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang, Zan","raw_affiliation_strings":["Institute of Geology and Geophysics, Chinese Academy of Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geology and Geophysics, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zheng, Bowen","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng, Bowen","raw_affiliation_strings":["Institute of Geology and Geophysics, Chinese Academy of Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geology and Geophysics, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yang, Yue","orcid":null},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang, Yue","raw_affiliation_strings":["China University of Geosciences (Beijing)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China University of Geosciences (Beijing)","institution_ids":["https://openalex.org/I3125743391"]}]},{"author_position":"last","author":{"id":null,"display_name":"Tang, Fengjiao","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tang, Fengjiao","raw_affiliation_strings":["Institute of Geology and Geophysics, Chinese Academy of Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geology and Geophysics, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/landslide","display_name":"Landslide","score":0.7829999923706055},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5891000032424927},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5774999856948853},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4648999869823456},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.45809999108314514},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.4300999939441681},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.41819998621940613},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.30559998750686646}],"concepts":[{"id":"https://openalex.org/C186295008","wikidata":"https://www.wikidata.org/wiki/Q167903","display_name":"Landslide","level":2,"score":0.7829999923706055},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6078000068664551},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5891000032424927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5787000060081482},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5774999856948853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5005000233650208},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4648999869823456},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.45809999108314514},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.4300999939441681},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.4165000021457672},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.3813999891281128},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.30559998750686646},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28299999237060547},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2800000011920929},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.27489998936653137},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2624000012874603},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/g6nd-zt37","is_oa":true,"landing_page_url":"https://doi.org/10.21227/g6nd-zt37","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/g6nd-zt37","is_oa":true,"landing_page_url":"https://doi.org/10.21227/g6nd-zt37","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[{"score":0.6625443696975708,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Optical":[0],"remote":[1,30,101,226],"sensing":[2,31,102,227],"images,":[3],"with":[4,104,130,177],"their":[5],"high":[6],"spatial":[7],"resolution":[8],"and":[9,22,38,59,81,85,91,181,211],"wide":[10],"coverage,":[11],"have":[12,217],"emerged":[13],"as":[14],"invaluable":[15],"tools":[16],"for":[17,69,95,114,123,159,185,220],"landslide":[18,26,43,97,124,161,222],"analysis.":[19],"Visual":[20],"interpretation":[21],"manual":[23],"delimitation":[24,41,126],"of":[25,42,87,135,155,190,207],"areas":[27,44,98],"in":[28,54,72,99,175,194,225],"optical":[29,100],"images":[32,103],"by":[33,46],"human":[34],"is":[35,139,144,182],"labor":[36],"intensive":[37],"inefficient.":[39],"Automatic":[40],"empowered":[45],"deep":[47,66,116],"learning":[48,67,117],"methods":[49],"has":[50],"drawn":[51],"tremendous":[52],"attention":[53],"recent":[55],"years.":[56],"Mask":[57,89,148,168],"R-CNN":[58,90,149,169],"U-Net":[60,93,179],"are":[61],"the":[62,83,88,92,147,153,167,178,191,208],"two":[63],"most":[64],"popular":[65],"frameworks":[68],"image":[70],"segmentation":[71],"computer":[73],"vision.":[74],"In":[75],"this":[76,200],"study,":[77],"we":[78],"systematically":[79],"compare":[80],"evaluate":[82],"performance":[84,174],"adaptability":[86],"models":[94,118],"delimiting":[96],"various":[105],"resolutions":[106],"across":[107,213],"regions":[108,122,129],"using":[109],"statistical":[110],"metrics.":[111],"A":[112,141],"workflow":[113],"transferring":[115],"pretrained":[119],"on":[120,127],"other":[121],"area":[125],"new":[128],"a":[131],"relatively":[132],"small":[133],"number":[134],"annotated":[136],"training":[137],"samples":[138],"developed.":[140],"post-processing":[142],"module":[143],"integrated":[145],"into":[146],"architecture":[150],"to":[151],"address":[152],"challenge":[154],"overlapping":[156],"mask":[157],"predictions":[158],"individual":[160],"objects.":[162],"The":[163,196],"results":[164],"indicate":[165],"that":[166],"model":[170,180],"exhibits":[171],"superior":[172],"overall":[173],"comparison":[176],"more":[183],"suitable":[184],"tasks":[186],"requiring":[187],"detailed":[188],"delineation":[189],"object":[192],"outlines":[193],"images.":[195,228],"insights":[197],"gained":[198],"from":[199],"study":[201],"not":[202],"only":[203],"advance":[204],"our":[205],"understanding":[206],"models'":[209],"generalizability":[210],"robustness":[212],"regions,":[214],"but":[215],"also":[216],"practical":[218],"implications":[219],"large-scale":[221],"inventory":[223],"mapping":[224]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
