{"id":"https://openalex.org/W7159657534","doi":"https://doi.org/10.48550/arxiv.2604.27704","title":"A generalised pre-training strategy for deep learning networks in semantic segmentation of remotely sensed images","display_name":"A generalised pre-training strategy for deep learning networks in semantic segmentation of remotely sensed images","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159657534","doi":"https://doi.org/10.48550/arxiv.2604.27704"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27704","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27704","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.2604.27704","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134931250","display_name":"Yuan Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028436114","display_name":"Yuanzhi Cai","orcid":"https://orcid.org/0000-0002-7005-5870"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Yuanzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134972133","display_name":"Jagannath Aryal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aryal, Jagannath","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102817214","display_name":"Qinfeng Zhu","orcid":"https://orcid.org/0009-0002-4847-3555"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Qinfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134972000","display_name":"Hong Huang (119275)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134932390","display_name":"Cheng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134976994","display_name":"Lei Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Lei","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":0.5037999749183655,"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":0.5037999749183655,"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":0.3025999963283539,"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":0.0997999981045723,"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/deep-learning","display_name":"Deep learning","score":0.7817999720573425},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6798999905586243},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.49630001187324524},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45750001072883606},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4415000081062317},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32350000739097595}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8015000224113464},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7817999720573425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7732999920845032},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6798999905586243},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45750001072883606},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39890000224113464},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3305000066757202},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2712000012397766}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27704","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27704","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.2604.27704","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27704","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,25,35,125,129,134,167,199],"segmentation":[2,150],"of":[3,27,128],"remotely":[4,49],"sensed":[5,50],"images,":[6],"deep":[7,137],"learning":[8,114,138],"models":[9,30,139],"are":[10,140],"typically":[11],"pre-trained":[12,130,141],"using":[13],"large":[14,36],"image":[15,64],"databases":[16],"like":[17],"ImageNet":[18,143],"before":[19],"fine-tuned":[20,29,146],"on":[21,142,147,175],"domain-specific":[22,63,115],"datasets.":[23],"However,":[24,73],"performance":[26],"these":[28,84,96],"is":[31,77],"often":[32,78,86],"hindered":[33],"by":[34],"domain":[37],"gaps":[38],"(i.e.,":[39],"differences":[40],"in":[41,117],"scenes":[42,154],"and":[43,48,83,144,155,161,212],"modalities)":[44],"between":[45],"ImageNet's":[46],"images":[47,51],"being":[52],"processed.":[53],"Therefore,":[54],"many":[55],"researchers":[56],"have":[57],"undertaken":[58],"efforts":[59],"to":[60,69,90,108,172,208],"establish":[61],"large-scale":[62],"datasets":[65,76,85,151],"for":[66,182,186,190,194,201],"pre-training,":[67,122],"aiming":[68],"enhance":[70],"model":[71,111,206],"performance.":[72],"establishing":[74],"such":[75],"challenging,":[79],"requiring":[80],"significant":[81],"effort,":[82],"exhibit":[87],"limited":[88],"generaliza-bility":[89],"other":[91],"application":[92],"scenarios.":[93],"To":[94,132],"address":[95],"issues,":[97],"this":[98],"study":[99],"introduces":[100],"a":[101,110,118,203],"novel":[102],"yet":[103],"simple":[104],"pre-training":[105,119,169],"strategy":[106,170],"designed":[107],"guide":[109],"away":[112],"from":[113],"features":[116],"dataset":[120],"during":[121],"thereby":[123],"improving":[124],"generalisation":[126],"ability":[127],"model.":[131],"evaluate":[133],"strategy's":[135],"effectiveness,":[136],"subsequently":[145],"four":[148,177],"semantic":[149],"with":[152],"diverse":[153],"modalities,":[156],"including":[157],"iSAID,":[158,183],"MFNet,":[159,187],"PST900":[160],"Potsdam.":[162,195],"Experimental":[163],"results":[164],"show":[165],"that":[166],"proposed":[168],"led":[171],"state-of-the-art":[173],"accuracies":[174],"all":[176],"datasets,":[178],"namely":[179],"67.4%":[180],"mIoU":[181,185,189],"56.9%":[184],"84.22%":[188],"PST900,":[191],"91.88%":[192],"mF1":[193],"This":[196],"research":[197],"lays":[198],"groundwork":[200],"developing":[202],"unified":[204],"foundation":[205],"applicable":[207],"both":[209],"computer":[210],"vision":[211],"remote":[213],"sensing":[214],"applications.":[215]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-02T00:00:00"}
