{"id":"https://openalex.org/W4321376170","doi":"https://doi.org/10.3390/rs15041112","title":"Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine","display_name":"Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine","publication_year":2023,"publication_date":"2023-02-17","ids":{"openalex":"https://openalex.org/W4321376170","doi":"https://doi.org/10.3390/rs15041112"},"language":"en","primary_location":{"id":"doi:10.3390/rs15041112","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15041112","pdf_url":"https://www.mdpi.com/2072-4292/15/4/1112/pdf?version=1676881405","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/2072-4292/15/4/1112/pdf?version=1676881405","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027045535","display_name":"Xingyuan Xiao","orcid":"https://orcid.org/0000-0003-1611-4531"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingyuan Xiao","raw_affiliation_strings":["College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"],"affiliations":[{"raw_affiliation_string":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083561757","display_name":"Linlong Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linlong Jiang","raw_affiliation_strings":["College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"],"affiliations":[{"raw_affiliation_string":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082151468","display_name":"Yaqun Liu","orcid":"https://orcid.org/0000-0003-2330-5651"},"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"]},{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yaqun Liu","raw_affiliation_strings":["Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","institution_ids":["https://openalex.org/I4210160793","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064358717","display_name":"Guozhen Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guozhen Ren","raw_affiliation_strings":["Shandong Ruizhi Flight Control Technology, Co., Ltd., Qingdao 266500, China"],"affiliations":[{"raw_affiliation_string":"Shandong Ruizhi Flight Control Technology, Co., Ltd., Qingdao 266500, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5082151468"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210160793"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":4.6667,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.94654107,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"15","issue":"4","first_page":"1112","last_page":"1112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6571519374847412},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5786425471305847},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5772912502288818},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5323778986930847},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.501204252243042},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.4729318916797638},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44866877794265747},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42106425762176514},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4183049201965332},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4128047823905945},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3305431604385376},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29747533798217773},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23138165473937988},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.17750000953674316},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14636263251304626},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1361522376537323}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6571519374847412},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5786425471305847},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5772912502288818},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5323778986930847},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.501204252243042},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.4729318916797638},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44866877794265747},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42106425762176514},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4183049201965332},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4128047823905945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3305431604385376},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29747533798217773},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23138165473937988},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.17750000953674316},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14636263251304626},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1361522376537323},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15041112","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15041112","pdf_url":"https://www.mdpi.com/2072-4292/15/4/1112/pdf?version=1676881405","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:69cdfb8bda834a8983f796eee20f44b4","is_oa":true,"landing_page_url":"https://doaj.org/article/69cdfb8bda834a8983f796eee20f44b4","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 15, Iss 4, p 1112 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/4/1112/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15041112","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","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15041112","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15041112","pdf_url":"https://www.mdpi.com/2072-4292/15/4/1112/pdf?version=1676881405","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":[{"score":0.8500000238418579,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[{"id":"https://openalex.org/G4194544158","display_name":null,"funder_award_id":"42201289","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G6033570301","display_name":null,"funder_award_id":"42271273","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7279705090","display_name":null,"funder_award_id":"2021M700143","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7862431446","display_name":null,"funder_award_id":"42201289","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8579567740","display_name":null,"funder_award_id":"2021M700143","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G8904865214","display_name":null,"funder_award_id":"42271273","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4321376170.pdf"},"referenced_works_count":80,"referenced_works":["https://openalex.org/W116902681","https://openalex.org/W561088580","https://openalex.org/W1964146567","https://openalex.org/W1972078955","https://openalex.org/W1978496001","https://openalex.org/W2005966471","https://openalex.org/W2008085934","https://openalex.org/W2075271931","https://openalex.org/W2078587853","https://openalex.org/W2084744129","https://openalex.org/W2085992718","https://openalex.org/W2097419478","https://openalex.org/W2104763670","https://openalex.org/W2111435577","https://openalex.org/W2113410727","https://openalex.org/W2118246710","https://openalex.org/W2128160875","https://openalex.org/W2129331467","https://openalex.org/W2138973222","https://openalex.org/W2140908571","https://openalex.org/W2148333466","https://openalex.org/W2261059368","https://openalex.org/W2307094448","https://openalex.org/W2341283081","https://openalex.org/W2344186514","https://openalex.org/W2397355252","https://openalex.org/W2522991697","https://openalex.org/W2552805558","https://openalex.org/W2570150535","https://openalex.org/W2572164317","https://openalex.org/W2612271937","https://openalex.org/W2612601779","https://openalex.org/W2700341059","https://openalex.org/W2725897987","https://openalex.org/W2747135594","https://openalex.org/W2752411461","https://openalex.org/W2767953525","https://openalex.org/W2768148511","https://openalex.org/W2772365113","https://openalex.org/W2788340823","https://openalex.org/W2891430670","https://openalex.org/W2894214161","https://openalex.org/W2895946745","https://openalex.org/W2900217217","https://openalex.org/W2900680941","https://openalex.org/W2919016474","https://openalex.org/W2921701549","https://openalex.org/W2927615156","https://openalex.org/W2947716040","https://openalex.org/W2988073690","https://openalex.org/W2998303974","https://openalex.org/W3003630756","https://openalex.org/W3004441798","https://openalex.org/W3017113337","https://openalex.org/W3033448968","https://openalex.org/W3034843493","https://openalex.org/W3089217810","https://openalex.org/W3106378566","https://openalex.org/W3119404649","https://openalex.org/W3127060672","https://openalex.org/W3127557648","https://openalex.org/W3134068221","https://openalex.org/W3136913970","https://openalex.org/W3163231391","https://openalex.org/W3193406951","https://openalex.org/W3193949974","https://openalex.org/W4206941194","https://openalex.org/W4220734717","https://openalex.org/W4220788410","https://openalex.org/W4220802274","https://openalex.org/W4225120894","https://openalex.org/W4283324343","https://openalex.org/W4292452020","https://openalex.org/W4292553629","https://openalex.org/W4306651403","https://openalex.org/W4313508348","https://openalex.org/W4316040105","https://openalex.org/W6754453871","https://openalex.org/W6760223068","https://openalex.org/W6773380076"],"related_works":["https://openalex.org/W2030799363","https://openalex.org/W2950183183","https://openalex.org/W2341338763","https://openalex.org/W2288425735","https://openalex.org/W2349923317","https://openalex.org/W2894081631","https://openalex.org/W2986063033","https://openalex.org/W2040439981","https://openalex.org/W2472888994","https://openalex.org/W1791724651"],"abstract_inverted_index":{"Reliable":[0],"crop":[1,21,49,95,248,265],"type":[2,249,266],"classification":[3,22,50,109,181,267],"supports":[4],"the":[5,34,71,94,98,103,113,123,143,147,157,219,229,260],"scientific":[6],"basis":[7],"for":[8,89,131,178,243],"food":[9],"security":[10],"and":[11,27,39,59,65,67,77,111,142,169,187,212,234,238,246,262],"sustainable":[12,271],"agricultural":[13,272],"development.":[14,273],"However,":[15],"it":[16,213],"still":[17],"lacks":[18],"a":[19],"limited-samples-based":[20,247],"method":[23],"which":[24],"is":[25],"labor-":[26],"time-efficient.":[28],"To":[29],"this":[30],"end,":[31],"we":[32],"used":[33],"Google":[35],"Earth":[36],"Engine":[37],"(GEE)":[38],"Sentinel-1A/B":[40],"SAR":[41],"time":[42],"series":[43],"to":[44,92,116,197,208,259],"develop":[45],"eight":[46,180],"types":[47,96],"of":[48,57,63,70,253,264],"strategies":[51,182],"based":[52],"on":[53],"different":[54,61,68,87],"sampling":[55,155,159],"methods":[56],"central":[58,158],"scattered,":[60],"perspectives":[62],"object-based":[64,144],"pixel-based,":[66],"classifiers":[69],"Time-Weighted":[72],"Dynamic":[73],"Time":[74],"Warping":[75],"(TWDTW)":[76],"Random":[78],"Forest":[79],"(RF).":[80],"We":[81,105],"carried":[82],"out":[83],"30-times":[84],"classifications":[85,135],"with":[86,172,183],"samples":[88,205],"each":[90],"strategy":[91],"classify":[93],"at":[97],"North":[99],"Dakota\u2013Minnesota":[100],"border":[101],"in":[102,153,166],"U.S.":[104],"then":[106],"compared":[107],"their":[108],"accuracies":[110,138,176],"assessed":[112],"accuracy":[114,225],"sensitivity":[115],"sample":[117,185,198,220],"size.":[118],"The":[119,175,224,251],"results":[120],"found":[121],"that":[122,228],"TWDTW":[124,145,161,188,207,217,230],"generally":[125],"performed":[126,151,162,214],"better":[127,152,163,215],"than":[128,139,156,164,206,216],"RF,":[129],"especially":[130],"small-sample":[132],"classification.":[133,250],"Object-based":[134],"had":[136,146],"higher":[137],"pixel-based":[140],"classifications,":[141],"highest":[148],"accuracy.":[149],"RF":[150,165,193,201],"scattered":[154],"strategy.":[160],"distinguishing":[167],"soybean":[168],"dry":[170],"bean":[171],"similar":[173],"curves.":[174],"improved":[177],"all":[179],"increasing":[184],"size,":[186],"was":[189,194],"more":[190,195,204],"robust,":[191],"while":[192],"sensitive":[196],"size":[199,221],"change.":[200],"required":[202],"many":[203],"achieve":[209],"satisfactory":[210],"accuracy,":[211],"when":[218],"exceeded":[222],"50.":[223],"comparisons":[226],"indicated":[227],"has":[231,239],"stronger":[232],"temporal":[233],"spatial":[235],"generalization":[236],"capabilities":[237],"high":[240],"potential":[241],"applications":[242],"early,":[244],"historical,":[245],"findings":[252],"our":[254],"research":[255],"are":[256],"worthwhile":[257],"contributions":[258],"methodology":[261],"practices":[263],"as":[268,270],"well":[269]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}