{"id":"https://openalex.org/W7162460193","doi":"https://doi.org/10.48550/arxiv.2605.24014","title":"SkySeg: Collaborative Onboard Semantic Segmentation with Heterogeneous UAVs in the Wild","display_name":"SkySeg: Collaborative Onboard Semantic Segmentation with Heterogeneous UAVs in the Wild","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7162460193","doi":"https://doi.org/10.48550/arxiv.2605.24014"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24014","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24014","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.24014","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134095065","display_name":"Anqi Lu","orcid":"https://orcid.org/0000-0002-8835-6938"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Anqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137030621","display_name":"Yun Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137038990","display_name":"Youbing Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Youbing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136991964","display_name":"Zhiqiang Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Zhiqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137004941","display_name":"Jie Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137071296","display_name":"Zhijun Li","orcid":"https://orcid.org/0009-0004-8456-7107"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhijun","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/T10036","display_name":"Advanced Neural Network Applications","score":0.4065999984741211,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.4065999984741211,"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/T11133","display_name":"UAV Applications and Optimization","score":0.32179999351501465,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.025200000032782555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7838000059127808},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6841999888420105},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4832000136375427},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4453999996185303},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.3853999972343445},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.3833000063896179},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3449999988079071}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7838000059127808},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7663999795913696},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6841999888420105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5224000215530396},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48809999227523804},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4832000136375427},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4453999996185303},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3853999972343445},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3691999912261963},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.26339998841285706},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.2614000141620636},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24014","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24014","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.24014","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24014","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"score":0.43829789757728577,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"demand":[1],"for":[2,17],"unmanned":[3],"aerial":[4],"vehicle":[5],"(UAV)-based":[6],"image":[7],"acquisition":[8],"and":[9,35,71,105,178],"analysis":[10,25],"has":[11],"surged,":[12],"with":[13,128],"UAVs":[14,65],"increasingly":[15],"utilized":[16],"semantic":[18,48,69,111],"segmentation":[19,49,112,143,174],"tasks.":[20],"To":[21,87],"meet":[22],"the":[23,40,62,83,186],"real-time":[24,68],"requirements":[26],"of":[27,64,153],"UAV":[28,52],"remote":[29],"sensing":[30],"missions,":[31],"performing":[32],"onboard":[33,110,173],"computation":[34],"making":[36],"decisions":[37],"based":[38],"on":[39,50],"results":[41,160],"is":[42],"a":[43,95,135,180],"natural":[44],"approach.":[45],"However,":[46],"deploying":[47],"resource-constrained":[51],"platforms":[53],"presents":[54],"two":[55],"significant":[56],"challenges:":[57],"1)":[58],"hardware":[59],"constraints":[60],"limit":[61],"ability":[63],"to":[66,108,141],"perform":[67],"segmentation,":[70],"2)":[72],"environmental":[73],"variations":[74],"during":[75],"flight":[76,106],"cause":[77],"data":[78,155],"distribution":[79,151],"shifts,":[80],"deviating":[81],"from":[82],"original":[84],"training":[85],"data.":[86],"address":[88],"these":[89],"issues,":[90],"this":[91],"paper":[92],"introduces":[93],"SkySeg,":[94],"heterogeneous":[96],"multi-UAV":[97],"air-air":[98],"cooperation":[99],"framework":[100,165],"that":[101,162],"integrates":[102],"computer":[103],"vision":[104],"pattern":[107],"enable":[109],"using":[113],"low-cost":[114],"sensors.":[115],"SkySeg":[116,164],"employs":[117],"an":[118],"efficient":[119],"information":[120],"fusion":[121],"inference":[122,167],"method,":[123],"combining":[124],"low-definition,":[125],"wide-area":[126],"images":[127],"high-definition,":[129],"focused-area":[130],"images.":[131],"Additionally,":[132],"it":[133],"incorporates":[134],"cross-device":[136],"test-time":[137],"adaptation":[138],"(TTA)":[139],"strategy":[140],"enhance":[142],"performance":[144],"in":[145,185],"dynamic":[146],"environments":[147],"by":[148,169,176],"collaboratively":[149],"addressing":[150],"shifts":[152],"test":[154],"streams":[156],"across":[157],"UAVs.":[158],"Experimental":[159],"demonstrate":[161],"our":[163],"accelerates":[166],"latency":[168],"approximately":[170],"3.6x,":[171],"improves":[172],"accuracy":[175,183],"5.91\\%,":[177],"achieves":[179],"10.91\\%":[181],"average":[182],"gain":[184],"wild.":[187]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-27T00:00:00"}