{"id":"https://openalex.org/W4402811474","doi":"https://doi.org/10.1109/icecai62591.2024.10674750","title":"Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery","display_name":"Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery","publication_year":2024,"publication_date":"2024-05-31","ids":{"openalex":"https://openalex.org/W4402811474","doi":"https://doi.org/10.1109/icecai62591.2024.10674750"},"language":"en","primary_location":{"id":"doi:10.1109/icecai62591.2024.10674750","doi":"https://doi.org/10.1109/icecai62591.2024.10674750","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecai62591.2024.10674750","pdf_url":null,"source":{"id":"https://openalex.org/S4363608389","display_name":"International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1810.12813","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047800787","display_name":"Panfeng Li","orcid":"https://orcid.org/0000-0001-8586-0648","observed_orcids":["https://orcid.org/0000-0001-8586-0648"]},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Panfeng Li","raw_affiliation_strings":["University of Michigan,Department of Electrical and Computer Engineering,Ann Arbor,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan,Department of Electrical and Computer Engineering,Ann Arbor,USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001857687","display_name":"Youzuo Lin","orcid":"https://orcid.org/0000-0001-7337-6760","observed_orcids":["https://orcid.org/0000-0001-7337-6760"]},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Youzuo Lin","raw_affiliation_strings":["Geophysics Group Los Alamos National Laboratory,Ann Arbor,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Geophysics Group Los Alamos National Laboratory,Ann Arbor,USA","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018235008","display_name":"Emily S. Schultz-Fellenz","orcid":"https://orcid.org/0000-0002-8181-5003","observed_orcids":["https://orcid.org/0000-0002-8181-5003"]},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Emily Schultz-Fellenz","raw_affiliation_strings":["Geology and Geochemistry Research Team, Los Alamos National Laboratory,Los Alamos,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Geology and Geochemistry Research Team, Los Alamos National Laboratory,Los Alamos,USA","institution_ids":["https://openalex.org/I1343871089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.9231,"has_fulltext":true,"cited_by_count":48,"citation_normalized_percentile":{"value":0.96644822,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":87,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"15","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.8784000277519226,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.8784000277519226,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10480000078678131,"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":0.01360000018030405,"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/semantic-segmentation","display_name":"semantic segmentation","score":0.9779999852180481},{"id":"https://openalex.org/keywords/aerial-imagery","display_name":"aerial imagery","score":0.925000011920929},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.8370000123977661},{"id":"https://openalex.org/keywords/encoder-decoder-architecture","display_name":"encoder-decoder architecture","score":0.7699999809265137},{"id":"https://openalex.org/keywords/multiscale-feature-extraction","display_name":"multi-scale feature extraction","score":0.8870000243186951},{"id":"https://openalex.org/keywords/contextual-semantics","display_name":"contextual semantics","score":0.7129999995231628},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.6019999980926514},{"id":"https://openalex.org/keywords/high-resolution-imagery","display_name":"high-resolution imagery","score":0.5199999809265137},{"id":"https://openalex.org/keywords/potsdam-dataset","display_name":"Potsdam dataset","score":0.7670000195503235},{"id":"https://openalex.org/keywords/vaihingen-dataset","display_name":"Vaihingen dataset","score":0.9980000257492065}],"concepts":[{"id":"https://openalex.org/C127532173","wikidata":"https://www.wikidata.org/wiki/Q179904","display_name":"Hourglass","level":2,"score":0.71649569272995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7150120139122009},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.6622554659843445},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6495566964149475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5995510816574097},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.4853462278842926},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46956300735473633},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4668009877204895},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4126843214035034},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.20465609431266785},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.18467316031455994},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.16545319557189941},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icecai62591.2024.10674750","doi":"https://doi.org/10.1109/icecai62591.2024.10674750","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecai62591.2024.10674750","pdf_url":null,"source":{"id":"https://openalex.org/S4363608389","display_name":"International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1810.12813","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.12813","pdf_url":"https://arxiv.org/pdf/1810.12813","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.1810.12813","doi":"https://doi.org/10.48550/arxiv.1810.12813","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1810.12813","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":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1810.12813","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.12813","pdf_url":"https://arxiv.org/pdf/1810.12813","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":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338304","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402811474.pdf","grobid_xml":"https://content.openalex.org/works/W4402811474.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W1910657905","https://openalex.org/W2194775991","https://openalex.org/W2271840356","https://openalex.org/W2307770531","https://openalex.org/W2737129951","https://openalex.org/W2738232694","https://openalex.org/W2770904017","https://openalex.org/W2785885239","https://openalex.org/W2804451008","https://openalex.org/W2895340641","https://openalex.org/W2955058313","https://openalex.org/W2963727650","https://openalex.org/W2964121744","https://openalex.org/W3105127913","https://openalex.org/W4206399361","https://openalex.org/W4221017459","https://openalex.org/W4391409518","https://openalex.org/W4392904609","https://openalex.org/W4399885365","https://openalex.org/W4399885374","https://openalex.org/W4403421504"],"related_works":["https://openalex.org/W2618943282","https://openalex.org/W4308235506","https://openalex.org/W2898760516","https://openalex.org/W3089769990","https://openalex.org/W6902183361","https://openalex.org/W3134260603","https://openalex.org/W4415452599","https://openalex.org/W2945127520","https://openalex.org/W3076759417","https://openalex.org/W4390263329"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,57,68,153],"for":[2,138],"aerial":[3],"imagery":[4,14],"is":[5],"a":[6,65,90],"challenging":[7,51],"and":[8,44,70,133,160],"important":[9],"problem":[10],"in":[11,79],"remotely":[12],"sensed":[13],"analysis.":[15],"In":[16,76],"recent":[17],"years,":[18],"with":[19],"the":[20,38,42,83,86,105,111,148,164,173],"success":[21],"of":[22,41,85,150],"deep":[23],"learning,":[24],"various":[25],"convolutional":[26],"neural":[27],"network":[28],"(CNN)":[29],"based":[30],"models":[31],"have":[32],"been":[33],"developed.":[34],"However,":[35],"due":[36],"to":[37,52,81,103,123,166],"varying":[39],"sizes":[40],"objects":[43],"imbalanced":[45],"class":[46],"labels,":[47],"it":[48,72,157],"can":[49,127],"be":[50],"obtain":[53],"accurate":[54],"pixel-wise":[55],"semantic":[56,67,152],"results.":[58],"To":[59,146],"address":[60],"those":[61],"challenges,":[62],"we":[63,88,155],"develop":[64],"novel":[66],"method":[69,171],"call":[71],"Contextual":[73],"Hourglass":[74],"Network.":[75],"our":[77,151,170],"method,":[78,154],"order":[80],"improve":[82],"robustness":[84],"prediction,":[87],"design":[89],"new":[91],"contextual":[92,106,118,141],"hourglass":[93,119],"module":[94],"which":[95],"incorporates":[96],"attention":[97],"mechanism":[98],"on":[99,158,176],"processed":[100],"low-resolution":[101],"featuremaps":[102],"exploit":[104,110],"semantics.":[107],"We":[108],"further":[109],"stacked":[112],"encoder-decoder":[113],"structure":[114],"by":[115],"connecting":[116],"multiple":[117],"modules":[120],"from":[121],"end":[122],"end.":[124],"This":[125],"architecture":[126],"effectively":[128],"extract":[129],"rich":[130],"multi-scale":[131],"features":[132],"add":[134],"more":[135],"feedback":[136],"loops":[137],"better":[139],"learning":[140],"semantics":[142],"through":[143],"intermediate":[144],"supervision.":[145],"demonstrate":[147],"efficacy":[149],"test":[156],"Potsdam":[159],"Vaihingen":[161],"datasets.":[162],"Through":[163],"comparisons":[165],"other":[167],"baseline":[168],"methods,":[169],"yields":[172],"best":[174],"results":[175],"overall":[177],"performance.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":35},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2024-09-25T00:00:00"}