{"id":"https://openalex.org/W3136978210","doi":null,"title":"SaNet: Scale-aware neural Network for Parsing Multiple Spatial Resolution Aerial Images.","display_name":"SaNet: Scale-aware neural Network for Parsing Multiple Spatial Resolution Aerial Images.","publication_year":2021,"publication_date":"2021-03-14","ids":{"openalex":"https://openalex.org/W3136978210","mag":"3136978210"},"language":"en","primary_location":{"id":"mag:3136978210","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2103.07935","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2103.07935","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045212381","display_name":"Libo Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Libo Wang","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5045212381"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"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":0.8109999895095825,"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":0.8109999895095825,"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":0.11710000038146973,"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":0.06260000169277191,"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/aerial-image-segmentation","display_name":"aerial image segmentation","score":0.6010000109672546},{"id":"https://openalex.org/keywords/scale-invariant-features","display_name":"scale-invariant features","score":0.9309999942779541},{"id":"https://openalex.org/keywords/urban-scene-understanding","display_name":"urban scene understanding","score":0.8550000190734863},{"id":"https://openalex.org/keywords/remote-sensing-imagery","display_name":"remote sensing images","score":0.40799999237060547}],"concepts":[{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.7505798935890198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.676173985004425},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6442290544509888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6441735029220581},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5759295225143433},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5331904888153076},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5327679514884949},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5036119818687439},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4619600772857666},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45468756556510925},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4504458010196686},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.42007166147232056},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4176059067249298},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.41709524393081665},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3691069781780243},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.28434503078460693},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2537360191345215},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12508437037467957},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3136978210","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2103.07935","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3136978210","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2103.07935","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":82,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1990372984","https://openalex.org/W1997305839","https://openalex.org/W2000803298","https://openalex.org/W2058860308","https://openalex.org/W2089716607","https://openalex.org/W2113199901","https://openalex.org/W2151103935","https://openalex.org/W2155632266","https://openalex.org/W2194775991","https://openalex.org/W2267317359","https://openalex.org/W2286929393","https://openalex.org/W2320738207","https://openalex.org/W2412782625","https://openalex.org/W2488187315","https://openalex.org/W2490270993","https://openalex.org/W2493402089","https://openalex.org/W2518108298","https://openalex.org/W2527276685","https://openalex.org/W2559966999","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2593886839","https://openalex.org/W2595964094","https://openalex.org/W2624614404","https://openalex.org/W2630837129","https://openalex.org/W2737129951","https://openalex.org/W2752790450","https://openalex.org/W2770904017","https://openalex.org/W2778539913","https://openalex.org/W2780740184","https://openalex.org/W2782522152","https://openalex.org/W2793461576","https://openalex.org/W2799213142","https://openalex.org/W2803620620","https://openalex.org/W2804732189","https://openalex.org/W2806664182","https://openalex.org/W2810004461","https://openalex.org/W2885093229","https://openalex.org/W2895340641","https://openalex.org/W2899101283","https://openalex.org/W2901442382","https://openalex.org/W2904311624","https://openalex.org/W2908510526","https://openalex.org/W2919115771","https://openalex.org/W2936599103","https://openalex.org/W2950925862","https://openalex.org/W2951005624","https://openalex.org/W2954469458","https://openalex.org/W2954896312","https://openalex.org/W2958449090","https://openalex.org/W2962891704","https://openalex.org/W2963091558","https://openalex.org/W2963294354","https://openalex.org/W2963446712","https://openalex.org/W2964080601","https://openalex.org/W2964217532","https://openalex.org/W2964309882","https://openalex.org/W2965383240","https://openalex.org/W2970773259","https://openalex.org/W2981849677","https://openalex.org/W2983208726","https://openalex.org/W3007268491","https://openalex.org/W3009297390","https://openalex.org/W3018169007","https://openalex.org/W3033983932","https://openalex.org/W3034971973","https://openalex.org/W3035339581","https://openalex.org/W3040304705","https://openalex.org/W3093142463","https://openalex.org/W3105127913","https://openalex.org/W3106250896","https://openalex.org/W3109998321","https://openalex.org/W3129042754","https://openalex.org/W3137572916","https://openalex.org/W3161825146","https://openalex.org/W3165423556","https://openalex.org/W3190334976","https://openalex.org/W3199325575","https://openalex.org/W3200075728","https://openalex.org/W3202757651"],"related_works":["https://openalex.org/W3152729632","https://openalex.org/W3189386569","https://openalex.org/W4200142823","https://openalex.org/W7103594983","https://openalex.org/W3213616908","https://openalex.org/W4324378972","https://openalex.org/W3045790313","https://openalex.org/W4285234694","https://openalex.org/W4319459168","https://openalex.org/W3198314649"],"abstract_inverted_index":{"Assigning":[0],"the":[1,11,22,30,43,60,79,90,93,114,124,138,149,155],"geospatial":[2,48],"objects":[3,49,87],"of":[4,47,154],"aerial":[5,32,74],"images":[6,33],"with":[7,78,108,130],"categorical":[8],"information":[9],"at":[10],"pixel-level":[12],"is":[13,121],"a":[14,65,96,118],"basic":[15],"task":[16],"in":[17,25,34],"urban":[18],"scene":[19],"understanding.":[20],"However,":[21],"huge":[23],"differencc":[24],"remote":[26],"sensing":[27],"sensors":[28],"makes":[29],"acqured":[31],"multiple":[35],"spatial":[36,55,131],"resolution":[37,56],"(MSR),":[38],"which":[39],"brings":[40],"two":[41,61],"issues:":[42],"increased":[44],"scale":[45,91],"variation":[46],"and":[50,85,143],"informative":[51,115],"feature":[52,99,116],"loss":[53],"as":[54],"drops.":[57],"To":[58,112],"address":[59],"issues,":[62],"we":[63],"propose":[64],"novel":[66],"scale-aware":[67],"neural":[68],"network":[69,100,125],"(SaNet)":[70],"for":[71],"parsing":[72],"MSR":[73],"images.":[75],"For":[76],"coping":[77],"imbalanced":[80],"segmentation":[81,152],"quality":[82,105],"between":[83],"larger":[84],"smaller":[86],"caused":[88],"by":[89],"variation,":[92],"SaNet":[94,157],"deploys":[95],"densely":[97],"connected":[98],"(DCFPN)":[101],"module":[102,120],"to":[103,126,159],"capture":[104],"multi-scale":[106],"context":[107],"large":[109],"receptive":[110],"fields.":[111],"alleviate":[113],"loss,":[117],"SFR":[119],"incorporated":[122],"into":[123],"learn":[127],"scale-invariant":[128],"features":[129],"relation":[132],"enhancement.":[133],"Extensive":[134],"experimental":[135],"results":[136],"on":[137],"ISPRS":[139,144],"Vaihingen":[140],"2D":[141,146],"Dataset":[142,147],"Potsdam":[145],"demonstrate":[148],"outstanding":[150],"cross-resolution":[151],"ability":[153],"proposed":[156],"compared":[158],"other":[160],"state-of-the-art":[161],"networks.":[162]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-03-29T00:00:00"}