{"id":"https://openalex.org/W2903977688","doi":"https://doi.org/10.1145/3287921.3287970","title":"Fully Residual Convolutional Neural Networks for Aerial Image Segmentation","display_name":"Fully Residual Convolutional Neural Networks for Aerial Image Segmentation","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2903977688","doi":"https://doi.org/10.1145/3287921.3287970","mag":"2903977688"},"language":"en","primary_location":{"id":"doi:10.1145/3287921.3287970","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3287921.3287970","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Ninth International Symposium on Information and Communication Technology  - SoICT 2018","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025948040","display_name":"Dinh Viet Sang","orcid":"https://orcid.org/0000-0002-9254-1327"},"institutions":[{"id":"https://openalex.org/I94518387","display_name":"Hanoi University of Science and Technology","ror":"https://ror.org/04nyv3z04","country_code":"VN","type":"education","lineage":["https://openalex.org/I94518387"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Dinh Viet Sang","raw_affiliation_strings":["Hanoi University of Science and Technology, Hanoi, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hanoi University of Science and Technology, Hanoi, Vietnam","institution_ids":["https://openalex.org/I94518387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104110381","display_name":"Nguy\u1ec5n \u0110\u1ee9c Minh","orcid":null},"institutions":[{"id":"https://openalex.org/I109689652","display_name":"FPT University","ror":"https://ror.org/03esj4g97","country_code":"VN","type":"education","lineage":["https://openalex.org/I109689652"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Nguyen Duc Minh","raw_affiliation_strings":["FPT Technology Research Institute, Hanoi, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"FPT Technology Research Institute, Hanoi, Vietnam","institution_ids":["https://openalex.org/I109689652"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.636,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.75366788,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"289","last_page":"296"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991999864578247,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8056540489196777},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.7562039494514465},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7496816515922546},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7060940265655518},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7051306962966919},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5383345484733582},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.5379621982574463},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.536766767501831},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5041114091873169},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.47042155265808105},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.4416925311088562},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43427830934524536},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.43199506402015686},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.32441139221191406},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23198971152305603},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10878494381904602}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8056540489196777},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.7562039494514465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7496816515922546},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7060940265655518},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7051306962966919},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5383345484733582},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.5379621982574463},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.536766767501831},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5041114091873169},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.47042155265808105},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.4416925311088562},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43427830934524536},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.43199506402015686},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.32441139221191406},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23198971152305603},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10878494381904602},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3287921.3287970","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3287921.3287970","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Ninth International Symposium on Information and Communication Technology  - SoICT 2018","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7799999713897705}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1909515874","https://openalex.org/W2097117768","https://openalex.org/W2099047584","https://openalex.org/W2112796928","https://openalex.org/W2126747775","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2241051737","https://openalex.org/W2511730936","https://openalex.org/W2618530766","https://openalex.org/W2745423625","https://openalex.org/W2783367000","https://openalex.org/W2793268137","https://openalex.org/W2886742956","https://openalex.org/W2963563573","https://openalex.org/W6725739302"],"related_works":["https://openalex.org/W2783354812","https://openalex.org/W4384112194","https://openalex.org/W2103009189","https://openalex.org/W4312958259","https://openalex.org/W4308259661","https://openalex.org/W4390813131","https://openalex.org/W2349383066","https://openalex.org/W4328132048","https://openalex.org/W1969901537","https://openalex.org/W4280568880"],"abstract_inverted_index":{"Semantic":[0,142],"segmentation":[1,156],"from":[2,23,129,139],"aerial":[3,73],"imagery":[4],"is":[5,38,122],"one":[6],"of":[7,15,30,35,43,81,112,119,157],"the":[8,13,31,39,79,88,109,132,135],"most":[9,32],"essential":[10],"tasks":[11,37],"in":[12,57,123,155],"field":[14],"remote":[16],"sensing":[17],"with":[18,91],"various":[19],"potential":[20],"applications":[21],"ranging":[22],"map":[24],"creation":[25],"to":[26,72],"intelligence":[27],"service.":[28],"One":[29],"challenging":[33],"factors":[34],"these":[36],"very":[40,58],"heterogeneous":[41],"appearance":[42],"artificial":[44],"objects":[45],"like":[46],"buildings,":[47],"cars":[48],"and":[49,104],"natural":[50],"entities":[51],"such":[52],"as":[53,108],"trees,":[54],"low":[55],"vegetation":[56],"high-resolution":[59],"digital":[60],"images.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65,100],"propose":[66],"an":[67],"efficient":[68],"deep":[69],"learning":[70],"approach":[71,77,147],"image":[74],"segmentation.":[75],"Our":[76],"utilizes":[78],"architecture":[80],"fully":[82],"convolutional":[83],"network":[84],"(FCN)":[85],"based":[86],"on":[87,134],"backbone":[89],"ResNet101":[90],"additional":[92],"upsampling":[93],"skip":[94],"connections.":[95],"Besides":[96],"typical":[97],"color":[98],"channels,":[99],"also":[101],"use":[102],"DSM":[103,106],"normalized":[105],"(nDSM)":[107],"input":[110],"data":[111],"our":[113,146],"models.":[114],"We":[115],"achieve":[116],"overall":[117],"accuracy":[118],"91%,":[120],"which":[121],"top":[124],"4":[125],"among":[126],"140":[127],"submissions":[128],"all":[130,152],"over":[131],"world":[133],"well-known":[136],"Vaihingen":[137],"dataset":[138],"ISPRS":[140],"2D":[141],"Labeling":[143],"Contest.":[144],"Especially,":[145],"yields":[148],"better":[149],"results":[150],"then":[151],"state-of-the-art":[153],"methods":[154],"car":[158],"objects.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
