{"id":"https://openalex.org/W2770858673","doi":"https://doi.org/10.1145/3149808.3149811","title":"Image-based classification of GPS noise level using convolutional neural networks for accurate distance estimation","display_name":"Image-based classification of GPS noise level using convolutional neural networks for accurate distance estimation","publication_year":2017,"publication_date":"2017-11-07","ids":{"openalex":"https://openalex.org/W2770858673","doi":"https://doi.org/10.1145/3149808.3149811","mag":"2770858673"},"language":"en","primary_location":{"id":"doi:10.1145/3149808.3149811","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3149808.3149811","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101559135","display_name":"Jamie Murphy","orcid":"https://orcid.org/0000-0002-3935-4303"},"institutions":[{"id":"https://openalex.org/I149530613","display_name":"Lynn University","ror":"https://ror.org/04nqv0v23","country_code":"US","type":"education","lineage":["https://openalex.org/I149530613"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Murphy","raw_affiliation_strings":["Lyft, Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lyft, Inc","institution_ids":["https://openalex.org/I149530613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029828331","display_name":"Yuanyuan Pao","orcid":null},"institutions":[{"id":"https://openalex.org/I149530613","display_name":"Lynn University","ror":"https://ror.org/04nqv0v23","country_code":"US","type":"education","lineage":["https://openalex.org/I149530613"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuanyuan Pao","raw_affiliation_strings":["Lyft, Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lyft, Inc","institution_ids":["https://openalex.org/I149530613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103459759","display_name":"Asif Haque","orcid":null},"institutions":[{"id":"https://openalex.org/I149530613","display_name":"Lynn University","ror":"https://ror.org/04nqv0v23","country_code":"US","type":"education","lineage":["https://openalex.org/I149530613"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Asif Haque","raw_affiliation_strings":["Lyft, Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lyft, Inc","institution_ids":["https://openalex.org/I149530613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149530613"],"apc_list":null,"apc_paid":null,"fwci":1.5171,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.81798919,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"10","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":1.0,"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":1.0,"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":0.9902999997138977,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9830999970436096,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/global-positioning-system","display_name":"Global Positioning System","score":0.7894476056098938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7583781480789185},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5895608067512512},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5505783557891846},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5106163620948792},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49250397086143494},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4747855067253113},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.467803031206131},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.46294131875038147},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4504002332687378},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3971499800682068},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.229082852602005}],"concepts":[{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.7894476056098938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7583781480789185},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5895608067512512},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5505783557891846},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5106163620948792},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49250397086143494},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4747855067253113},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.467803031206131},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.46294131875038147},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4504002332687378},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3971499800682068},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.229082852602005},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3149808.3149811","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3149808.3149811","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1985986451","https://openalex.org/W2071091794","https://openalex.org/W2084634989","https://openalex.org/W2135822894","https://openalex.org/W2155856926","https://openalex.org/W2156531019","https://openalex.org/W2163605009","https://openalex.org/W2280377497","https://openalex.org/W2520841747","https://openalex.org/W2541331368","https://openalex.org/W2919115771"],"related_works":["https://openalex.org/W3162200841","https://openalex.org/W2586280620","https://openalex.org/W2805505483","https://openalex.org/W2384744344","https://openalex.org/W4233932308","https://openalex.org/W1799694159","https://openalex.org/W2393169196","https://openalex.org/W2366610330","https://openalex.org/W1550496571","https://openalex.org/W2558515415"],"abstract_inverted_index":{"Accurate":[0],"route":[1,92,124,137],"prediction":[2],"and":[3,35,93,134,157,183,198],"distance":[4,100,193],"calculation":[5],"is":[6,75,151,158,176],"an":[7,154],"integral":[8],"part":[9],"of":[10,69,78,88,114,117,142,171,201],"processing":[11],"GPS":[12,27,73,95,132],"data,":[13],"particularly":[14],"in":[15,187],"the":[16,50,67,70,79,90,94,99,109,118,130,135,139,143,147,196],"ride-sharing":[17],"industry.":[18],"One":[19],"common":[20],"approach":[21],"has":[22],"been":[23],"to":[24,29,43,61,85,97,107,125,191],"map":[25,52,57],"match":[26],"data":[28,58,74,96,120,133],"estimate":[30,141],"driving":[31,144],"traces":[32,40,200],"under":[33],"noise":[34,110],"sparsity":[36],"conditions.":[37],"However,":[38],"map-matched":[39,91,136],"have":[41],"proven":[42],"be":[44,185],"at":[45],"most":[46],"as":[47,49,138,153],"good":[48],"underlying":[51,71],"data.":[53,173,204],"Incorrect":[54],"or":[55],"missing":[56],"can":[59,184],"lead":[60],"large,":[62],"improbable":[63],"deviations,":[64],"even":[65],"when":[66],"geometry":[68],"raw":[72,131],"within":[76],"tolerance":[77],"actual":[80,202],"route.":[81],"Ideally,":[82],"we":[83,103],"want":[84],"take":[86],"advantage":[87],"both":[89],"minimize":[98],"error.":[101],"Therefore,":[102],"propose":[104],"a":[105,161,168,177,188],"method":[106],"classify":[108],"level":[111],"(or":[112],"trustworthiness)":[113],"small":[115],"sub-sections":[116],"input":[119],"on":[121,167],"any":[122],"given":[123],"conditionally":[126],"select":[127],"between":[128,195],"using":[129],"best":[140],"path.":[145],"For":[146],"classifier,":[148],"each":[149],"section":[150],"treated":[152],"image":[155],"matrix":[156],"fed":[159],"through":[160],"convolutional":[162],"neural":[163],"network":[164],"trained":[165],"only":[166],"large":[169],"amount":[170],"synthetic":[172],"The":[174],"result":[175],"classifier":[178],"that":[179],"achieves":[180],"human-level":[181],"performance":[182],"used":[186],"real-time":[189],"system":[190],"reduce":[192],"errors":[194],"predicted":[197],"ground-truth":[199],"ride":[203]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}