{"id":"https://openalex.org/W4280532016","doi":"https://doi.org/10.48550/arxiv.2205.04160","title":"Improved-Flow Warp Module for Remote Sensing Semantic Segmentation","display_name":"Improved-Flow Warp Module for Remote Sensing Semantic Segmentation","publication_year":2022,"publication_date":"2022-05-09","ids":{"openalex":"https://openalex.org/W4280532016","doi":"https://doi.org/10.48550/arxiv.2205.04160"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2205.04160","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.04160","pdf_url":"https://arxiv.org/pdf/2205.04160","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2205.04160","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100761649","display_name":"Yinjie Zhang","orcid":"https://orcid.org/0000-0001-7760-0854","observed_orcids":["https://orcid.org/0000-0001-7760-0854"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yinjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100695128","display_name":"Yi Liu","orcid":"https://orcid.org/0000-0001-9993-0731","observed_orcids":["https://orcid.org/0000-0001-9993-0731"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100416207","display_name":"Wei Guo","orcid":"https://orcid.org/0000-0003-3735-6513","observed_orcids":["https://orcid.org/0000-0003-3735-6513","https://orcid.org/0000-0002-9936-1409","https://orcid.org/0009-0009-0836-7337","https://orcid.org/0009-0009-1105-3945"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Wei","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":true,"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":0.8295999765396118,"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":0.8295999765396118,"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":0.16169999539852142,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.00279999990016222,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/remote-sensing-semantic-segmentation","display_name":"remote sensing semantic segmentation","score":0.9020000100135803},{"id":"https://openalex.org/keywords/multi-scale-feature-alignment","display_name":"multi-scale feature alignment","score":0.8640000224113464},{"id":"https://openalex.org/keywords/aerial-image-classification","display_name":"aerial image classification","score":0.5950000286102295}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.808174729347229},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7266097664833069},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6535288691520691},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6240505576133728},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5900737047195435},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.5474810600280762},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5223094820976257},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5129861235618591},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4768977165222168},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4538605511188507},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4061616063117981},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40144971013069153},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.40078967809677124},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10921192169189453},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07712441682815552},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2205.04160","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.04160","pdf_url":"https://arxiv.org/pdf/2205.04160","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2205.04160","doi":"https://doi.org/10.48550/arxiv.2205.04160","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2205.04160","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,"listed_in":[],"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":"pmh:oai:arXiv.org:2205.04160","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.04160","pdf_url":"https://arxiv.org/pdf/2205.04160","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality education","score":0.44999998807907104}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324116","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319","https://openalex.org/W4390494008","https://openalex.org/W2053596378","https://openalex.org/W2922442631","https://openalex.org/W2168523118","https://openalex.org/W2073639911"],"abstract_inverted_index":{"Remote":[0],"sensing":[1,39,109],"semantic":[2,31,40,100],"segmentation":[3],"aims":[4],"to":[5,29,90],"assign":[6],"automatically":[7],"each":[8],"pixel":[9],"on":[10,106],"aerial":[11],"images":[12],"with":[13,49,83],"specific":[14],"label.":[15],"In":[16],"this":[17],"letter,":[18],"we":[19],"proposed":[20],"a":[21,67],"new":[22],"module,":[23],"called":[24],"improved-flow":[25,43],"warp":[26,44],"module":[27,45],"(IFWM),":[28],"adjust":[30],"feature":[32,51,87,93],"maps":[33],"across":[34],"different":[35],"scales":[36],"for":[37],"remote":[38,108],"segmentation.":[41,101],"The":[42],"is":[46],"applied":[47],"along":[48],"the":[50,55,62,73,76,80,84,92,97,112,115],"extraction":[52],"process":[53,89],"in":[54],"convolutional":[56],"neural":[57],"network.":[58],"First,":[59],"IFWM":[60],"computes":[61],"offsets":[63,81],"of":[64,75,99,117],"pixels":[65],"by":[66],"learnable":[68],"way,":[69],"which":[70,95],"can":[71],"alleviate":[72],"misalignment":[74],"multi-scale":[77],"features.":[78],"Second,":[79],"help":[82],"low-resolution":[85],"deep":[86],"up-sampling":[88],"improve":[91],"accordance,":[94],"boosts":[96],"accuracy":[98],"We":[102],"validate":[103],"our":[104,118],"method":[105],"several":[107],"datasets,":[110],"and":[111],"results":[113],"prove":[114],"effectiveness":[116],"method..":[119]},"counts_by_year":[],"updated_date":"2026-09-24T07:57:37.273135","created_date":"2025-10-10T00:00:00"}