{"id":"https://openalex.org/W2977856627","doi":"https://doi.org/10.1109/ijcnn.2019.8852009","title":"Fine-Grained Road Mining from Satellite Images with Bilateral Xception and DeepLab","display_name":"Fine-Grained Road Mining from Satellite Images with Bilateral Xception and DeepLab","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977856627","doi":"https://doi.org/10.1109/ijcnn.2019.8852009","mag":"2977856627"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5090184412","display_name":"Lele Cao","orcid":"https://orcid.org/0000-0002-5680-9031"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lele Cao","raw_affiliation_strings":["King Digital Entertainment, Activision Blizzard Group, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"King Digital Entertainment, Activision Blizzard Group, Stockholm, Sweden","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5090184412"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4396,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65756589,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"2019","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9950000047683716,"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"}},{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9890000224113464,"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/computer-science","display_name":"Computer science","score":0.7784699201583862},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.5831046104431152},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37403059005737305},{"id":"https://openalex.org/keywords/aerospace-engineering","display_name":"Aerospace engineering","score":0.0726865828037262},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07167977094650269}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7784699201583862},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.5831046104431152},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37403059005737305},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0726865828037262},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07167977094650269}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852009","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"mag:3035446857","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902269367962941","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W73112891","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1923697677","https://openalex.org/W1983896110","https://openalex.org/W1984288883","https://openalex.org/W2053077052","https://openalex.org/W2072783535","https://openalex.org/W2097117768","https://openalex.org/W2102048636","https://openalex.org/W2112583669","https://openalex.org/W2167069501","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2221898772","https://openalex.org/W2395611524","https://openalex.org/W2402144811","https://openalex.org/W2412782625","https://openalex.org/W2474996220","https://openalex.org/W2531409750","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2623331213","https://openalex.org/W2630837129","https://openalex.org/W2743136947","https://openalex.org/W2786492053","https://openalex.org/W2798925380","https://openalex.org/W2804199516","https://openalex.org/W2807508600","https://openalex.org/W2886934227","https://openalex.org/W2892094971","https://openalex.org/W2893801697","https://openalex.org/W2896457183","https://openalex.org/W2899319425","https://openalex.org/W2900570535","https://openalex.org/W2901772731","https://openalex.org/W2953384591","https://openalex.org/W2962835968","https://openalex.org/W2962891704","https://openalex.org/W2963341956","https://openalex.org/W2963881378","https://openalex.org/W2964121744","https://openalex.org/W2964309882","https://openalex.org/W2964333009","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6640295612","https://openalex.org/W6713134421","https://openalex.org/W6739696289","https://openalex.org/W6748481559","https://openalex.org/W6754123467","https://openalex.org/W6755207826","https://openalex.org/W7020680850"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"With":[0],"the":[1,24,41,55,99],"recent":[2],"development":[3],"of":[4,23,31,40,44,52,101],"remote":[5],"sensing":[6],"and":[7,12,36,57,104],"deep":[8],"learning":[9],"techniques,":[10],"automatic":[11],"robust":[13],"road":[14,127],"extraction":[15],"from":[16],"satellite":[17],"imaging":[18],"data":[19],"has":[20],"become":[21],"one":[22],"most":[25],"popular":[26],"topics":[27],"in":[28,69,125,142],"both":[29,102],"fields":[30],"Geographic":[32],"Information":[33],"System":[34],"(GIS)":[35],"Computer":[37],"Vision.":[38],"Despite":[39],"superior":[42,140],"performance":[43,79],"Convolutional":[45],"Neural":[46],"Networks":[47],"(DCNNs),":[48],"a":[49,92],"common":[50],"problem":[51],"choosing":[53],"between":[54],"classification":[56,103],"segmentation":[58,105],"DCNNs":[59,68,106,118],"still":[60],"remains.":[61],"By":[62],"comparing":[63],"two":[64],"state-of-the-art":[65],"baseline":[66],"classification/segmentation":[67],"several":[70],"industrial":[71,145],"application":[72],"scenarios,":[73],"we":[74,90],"illustrate":[75],"that":[76,88,96,135],"their":[77],"relative":[78],"may":[80],"vary,":[81],"leading":[82],"to":[83],"different":[84,126],"choices.":[85],"Based":[86],"on":[87],"observation,":[89],"propose":[91],"general":[93],"fusion":[94,137],"strategy":[95,138],"conveniently":[97],"combines":[98],"strength":[100],"using":[107],"an":[108],"end-to-end":[109],"network":[110],"architecture;":[111],"this":[112],"paradigm":[113],"only":[114],"requires":[115],"pre-train":[116],"segmentation/classification":[117],"once,":[119],"which":[120],"then":[121],"can":[122],"be":[123],"reused":[124],"feature":[128],"mining":[129],"tasks.":[130],"The":[131],"task-specific":[132],"experiments":[133],"show":[134],"our":[136],"guarantees":[139],"results":[141],"all":[143],"tested":[144],"scenarios.":[146]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
