{"id":"https://openalex.org/W7165968132","doi":"https://doi.org/10.48550/arxiv.2606.25312","title":"LEVIRDet: A Million-Scale 159-Category Dataset and Foundation Model for Universal Remote Sensing Object Detection","display_name":"LEVIRDet: A Million-Scale 159-Category Dataset and Foundation Model for Universal Remote Sensing Object Detection","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7165968132","doi":"https://doi.org/10.48550/arxiv.2606.25312"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.25312","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25312","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.25312","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139381773","display_name":"Qinzhe Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Qinzhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100764278","display_name":"Dongyu Wang","orcid":"https://orcid.org/0000-0001-6751-3589"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Dongyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139332254","display_name":"Haohan Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Haohan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139351854","display_name":"Jia Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139375726","display_name":"Zhenwei Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Zhenwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088611151","display_name":"Zhengxia Zou","orcid":"https://orcid.org/0000-0003-1774-552X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Zhengxia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.6209999918937683,"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":0.6209999918937683,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.22089999914169312,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.06260000169277191,"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/object-detection","display_name":"Object detection","score":0.7613999843597412},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5514000058174133},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.517300009727478},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.516700029373169},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5130000114440918},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5127999782562256},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.48989999294281006}],"concepts":[{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7613999843597412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7414000034332275},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.7084000110626221},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5514000058174133},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.517300009727478},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.516700029373169},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5127999782562256},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45500001311302185},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3919000029563904},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36739999055862427},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.3603000044822693},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.3434000015258789},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.311599999666214},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26269999146461487}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.25312","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25312","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.25312","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.25312","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Remote":[0],"sensing":[1,61,94,126,151,206],"object":[2,62,95,105,127,207],"detection":[3,15,63,96,120,146,171,208],"has":[4],"advanced":[5],"rapidly":[6],"with":[7,67],"the":[8,55,89,178,200,229],"development":[9,201],"of":[10,202],"large-scale":[11],"benchmarks":[12,25],"and":[13,20,46,57,74,107,141,143,215,220],"modern":[14],"architectures.":[16],"However,":[17],"existing":[18,92],"datasets":[19],"detectors":[21,38],"remain":[22],"fragmented.":[23],"Most":[24],"focus":[26],"on":[27,112,173,187],"limited":[28],"categories,":[29,69],"fixed":[30],"spatial":[31,213],"resolutions,":[32,214],"or":[33,166],"a":[34,79,118,144],"single":[35],"sensor,":[36],"while":[37],"still":[39],"struggle":[40],"to":[41,65],"work":[42],"across":[43,209],"different":[44],"sensors":[45],"categorical":[47],"systems.":[48],"In":[49,82],"this":[50,113,196],"paper,":[51],"we":[52,115],"introduce":[53],"LEVIRDet-159,":[54],"largest":[56,91],"most":[58],"comprehensive":[59],"remote":[60,93,125,150,205],"dataset":[64,219],"date,":[66],"159":[68],"2.56":[70],"million":[71],"bounding":[72],"boxes,":[73],"700k":[75],"fine-grained":[76],"annotations":[77],"under":[78,189],"multi-level":[80],"taxonomy.":[81],"each":[83,190],"key":[84],"scale":[85],"dimension,":[86],"LEVIRDet-159":[87],"exceeds":[88],"corresponding":[90],"dataset,":[97,114],"containing":[98],"approximately":[99],"(7x)":[100],"more":[101,104,109],"images,":[102],"(6x)":[103],"instances,":[106],"(4x)":[108],"categories.":[110],"Based":[111],"design":[116],"LEVIRDetNet,":[117],"scale-hierarchy-aware":[119],"foundation":[121],"model":[122],"for":[123,148],"universal":[124],"detection.":[128],"LEVIRDetNet":[129,157],"couples":[130],"online":[131],"visual":[132],"Ground":[133],"Sampling":[134],"Distance":[135],"(GSD)":[136],"prediction,":[137],"GSD-conditioned":[138],"query":[139],"modulation":[140],"allocation,":[142],"hierarchy-aware":[145],"head":[147],"mixed-granularity":[149],"supervision.":[152],"Under":[153],"stringent":[154],"evaluation":[155],"settings,":[156],"demonstrates":[158],"strong":[159],"cross-domain":[160],"generalization.":[161],"Even":[162],"without":[163],"target-domain":[164],"training":[165],"fine-tuning,":[167],"it":[168],"achieves":[169],"state-of-the-art":[170],"performance":[172],"9":[174],"external":[175],"benchmarks,":[176],"improving":[177],"strongest":[179],"fully":[180],"supervised":[181],"competing":[182],"methods":[183],"by":[184],"5.02":[185],"mAP":[186],"average":[188],"benchmark's":[191],"primary":[192],"metric.":[193],"We":[194],"hope":[195],"study":[197],"will":[198,223],"facilitate":[199],"strongly":[203],"generalizable":[204],"diverse":[210],"category":[211],"systems,":[212],"sensor":[216],"platforms.":[217],"The":[218],"trained":[221],"models":[222],"be":[224],"released":[225],"at":[226],"https://qinzheyang.github.io/LEVIRDet/,":[227],"accompanying":[228],"final":[230],"paper.":[231]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-26T00:00:00"}