{"id":"https://openalex.org/W3193481614","doi":"https://doi.org/10.1145/3447548.3467301","title":"Weakly Supervised Spatial Deep Learning based on Imperfect Vector Labels with Registration Errors","display_name":"Weakly Supervised Spatial Deep Learning based on Imperfect Vector Labels with Registration Errors","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3193481614","doi":"https://doi.org/10.1145/3447548.3467301","mag":"3193481614"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467301","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467301","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","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/A5021681759","display_name":"Zhe Jiang","orcid":"https://orcid.org/0000-0002-3576-6976"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhe Jiang","raw_affiliation_strings":["The University of Florida, Gainesville, FL, USA"],"affiliations":[{"raw_affiliation_string":"The University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070903469","display_name":"Wenchong He","orcid":"https://orcid.org/0000-0001-8115-1115"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenchong He","raw_affiliation_strings":["The University of Alabama, Tuscaloosa, AL, USA"],"affiliations":[{"raw_affiliation_string":"The University of Alabama, Tuscaloosa, AL, USA","institution_ids":["https://openalex.org/I17301866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026829153","display_name":"Marcus Stephen Kirby","orcid":null},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marcus Kirby","raw_affiliation_strings":["The University of Alabama, Tuscaloosa, AL, USA"],"affiliations":[{"raw_affiliation_string":"The University of Alabama, Tuscaloosa, AL, USA","institution_ids":["https://openalex.org/I17301866"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015182224","display_name":"Sultan Asiri","orcid":"https://orcid.org/0000-0002-7405-7646"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sultan Asiri","raw_affiliation_strings":["The University of Alabama, Tuscaloosa, AL, USA"],"affiliations":[{"raw_affiliation_string":"The University of Alabama, Tuscaloosa, AL, USA","institution_ids":["https://openalex.org/I17301866"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070582509","display_name":"Da Yan","orcid":"https://orcid.org/0000-0002-4653-0408"},"institutions":[{"id":"https://openalex.org/I32389192","display_name":"University of Alabama at Birmingham","ror":"https://ror.org/008s83205","country_code":"US","type":"education","lineage":["https://openalex.org/I32389192"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Da Yan","raw_affiliation_strings":["University of Alabama at Birmingham, Birmingham, AL, USA"],"affiliations":[{"raw_affiliation_string":"University of Alabama at Birmingham, Birmingham, AL, USA","institution_ids":["https://openalex.org/I32389192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5021681759"],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":0.2493,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.58151057,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"767","last_page":"775"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.994700014591217,"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":0.994700014591217,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9912999868392944,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9876999855041504,"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/computer-science","display_name":"Computer science","score":0.7563473582267761},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7178670167922974},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5907799005508423},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.574749767780304},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5664371252059937},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5276749730110168},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47250258922576904},{"id":"https://openalex.org/keywords/raster-graphics","display_name":"Raster graphics","score":0.4710552394390106},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44946250319480896},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4385186731815338},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.43633538484573364},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42247068881988525},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3235606551170349}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7563473582267761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7178670167922974},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5907799005508423},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.574749767780304},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5664371252059937},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5276749730110168},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47250258922576904},{"id":"https://openalex.org/C181844469","wikidata":"https://www.wikidata.org/wiki/Q182270","display_name":"Raster graphics","level":2,"score":0.4710552394390106},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44946250319480896},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4385186731815338},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.43633538484573364},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42247068881988525},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3235606551170349},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467301","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467301","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8855861821","display_name":null,"funder_award_id":"1850546,2008973,1951974","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1605688901","https://openalex.org/W1663973292","https://openalex.org/W1850843018","https://openalex.org/W1901129140","https://openalex.org/W1921293667","https://openalex.org/W2167460663","https://openalex.org/W2170122838","https://openalex.org/W2252268321","https://openalex.org/W2270414365","https://openalex.org/W2283140239","https://openalex.org/W2312198368","https://openalex.org/W2395811491","https://openalex.org/W2410883106","https://openalex.org/W2412782625","https://openalex.org/W2743556905","https://openalex.org/W2801965260","https://openalex.org/W2809182263","https://openalex.org/W2885139206","https://openalex.org/W2934379707","https://openalex.org/W2947263797","https://openalex.org/W2950510531","https://openalex.org/W2951094201","https://openalex.org/W2963881378","https://openalex.org/W2964292098","https://openalex.org/W2966851014","https://openalex.org/W2982631194","https://openalex.org/W3131264965","https://openalex.org/W3132759519","https://openalex.org/W3147666862","https://openalex.org/W3174008206","https://openalex.org/W4229511220","https://openalex.org/W4234552385","https://openalex.org/W4299828299"],"related_works":["https://openalex.org/W11765363","https://openalex.org/W9362070","https://openalex.org/W1383942","https://openalex.org/W2585641","https://openalex.org/W2712644","https://openalex.org/W1000462","https://openalex.org/W4608154","https://openalex.org/W11538522","https://openalex.org/W3647669","https://openalex.org/W8813607"],"abstract_inverted_index":{"This":[0],"paper":[1,170],"studies":[2],"weakly":[3,111],"supervised":[4,112],"learning":[5,36,104,113,174],"on":[6,11,110,116,141,150,153,177,200,214],"spatial":[7,173],"raster":[8,17,91],"data":[9],"based":[10,176,199],"imperfect":[12,21,66],"vector":[13,23,43,68,88,100,163,189,196,239],"training":[14],"labels.":[15,77],"Given":[16],"feature":[18,92],"imagery":[19],"and":[20,41,58,94,118,134,207,237],"(weak)":[22],"labels":[24,44,69,89],"with":[25,90],"location":[26,102],"registration":[27,143],"errors,":[28,144],"our":[29],"goal":[30],"is":[31,48,70,81,198],"to":[32,84,97,126],"learn":[33],"a":[34,172],"deep":[35,182],"model":[37],"for":[38],"pixel":[39,159,204],"classification":[40,235],"refine":[42],"simultaneously.":[45],"The":[46],"problem":[47,80],"important":[49],"in":[50,120,220,234],"many":[51],"geoscience":[52],"applications":[53],"such":[54],"as":[55],"streamline":[56],"delineation":[57],"road":[59],"mapping":[60],"from":[61],"earth":[62],"imagery,":[63],"where":[64],"annotating":[65],"coarse":[67],"far":[71],"more":[72],"efficient":[73],"than":[74],"drawing":[75],"precise":[76],"But":[78],"the":[79,85,95,158,167,202,208,228],"challenging":[82],"due":[83],"misalignment":[86,152],"of":[87,194,211],"pixels":[93],"need":[96],"infer":[98],"true":[99,188,195],"label":[101,121,124,142,151,190],"while":[103,186],"neural":[105,183],"network":[106,184],"parameters.":[107],"Existing":[108],"works":[109,139],"often":[114,148],"focus":[115,149],"noise":[117],"errors":[119],"semantics,":[122],"assuming":[123],"locations":[125,197],"be":[127],"either":[128],"correct":[129],"or":[130],"irrelevant":[131],"(e.g.,":[132],"identical":[133],"independently":[135],"distributed).":[136],"A":[137],"few":[138],"exist":[140],"but":[145],"these":[146],"methods":[147,233],"object":[154],"segment":[155],"boundaries":[156],"at":[157],"level":[160],"without":[161],"guaranteeing":[162],"continuity.":[164],"To":[165],"fill":[166],"gap,":[168],"this":[169],"proposes":[171],"framework":[175,230],"Expectation-Maximization":[178],"that":[179,227],"iteratively":[180],"updates":[181],"parameters":[185],"inferring":[187],"locations.":[191],"Specifically,":[192],"inference":[193],"both":[201],"current":[203],"class":[205],"predictions":[206],"geometric":[209],"properties":[210],"vectors.":[212],"Evaluations":[213],"real-world":[215],"high-resolution":[216],"remote":[217],"sensing":[218],"datasets":[219],"National":[221],"Hydrography":[222],"Dataset":[223],"(NHD)":[224],"refinement":[225],"show":[226],"proposed":[229],"outperforms":[231],"baseline":[232],"accuracy":[236],"refined":[238],"quality.":[240]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}