{"id":"https://openalex.org/W2088609836","doi":"https://doi.org/10.1145/2525314.2525465","title":"Image driven GPS trace analysis for road map inference","display_name":"Image driven GPS trace analysis for road map inference","publication_year":2013,"publication_date":"2013-11-05","ids":{"openalex":"https://openalex.org/W2088609836","doi":"https://doi.org/10.1145/2525314.2525465","mag":"2088609836"},"language":"en","primary_location":{"id":"doi:10.1145/2525314.2525465","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2525314.2525465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","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/A5101743528","display_name":"Jiangye Yuan","orcid":"https://orcid.org/0000-0001-8551-9925"},"institutions":[{"id":"https://openalex.org/I1289243028","display_name":"Oak Ridge National Laboratory","ror":"https://ror.org/01qz5mb56","country_code":"US","type":"facility","lineage":["https://openalex.org/I1289243028","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I4210159294"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiangye Yuan","raw_affiliation_strings":["Oak Ridge National Laboratory, Oak Ridge, Tennessee","Oak Ridge National Laboratory. Oak Ridge, Tennessee"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oak Ridge National Laboratory, Oak Ridge, Tennessee","institution_ids":["https://openalex.org/I1289243028"]},{"raw_affiliation_string":"Oak Ridge National Laboratory. Oak Ridge, Tennessee","institution_ids":["https://openalex.org/I1289243028"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068259837","display_name":"Anil Cheriyadat","orcid":null},"institutions":[{"id":"https://openalex.org/I1289243028","display_name":"Oak Ridge National Laboratory","ror":"https://ror.org/01qz5mb56","country_code":"US","type":"facility","lineage":["https://openalex.org/I1289243028","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I4210159294"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anil M. Cheriyadat","raw_affiliation_strings":["Oak Ridge National Laboratory, Oak Ridge, Tennessee","Oak Ridge National Laboratory. Oak Ridge, Tennessee"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oak Ridge National Laboratory, Oak Ridge, Tennessee","institution_ids":["https://openalex.org/I1289243028"]},{"raw_affiliation_string":"Oak Ridge National Laboratory. Oak Ridge, Tennessee","institution_ids":["https://openalex.org/I1289243028"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1289243028"],"apc_list":null,"apc_paid":null,"fwci":0.5952,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64502563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"480","last_page":"483"},"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":0.9921000003814697,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9085999727249146,"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/global-positioning-system","display_name":"Global Positioning System","score":0.8218084573745728},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7431020736694336},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6376537084579468},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.6157577633857727},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5863118767738342},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5699498653411865},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.5214221477508545},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5131345391273499},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33307939767837524},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1110493540763855},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08109891414642334}],"concepts":[{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.8218084573745728},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7431020736694336},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6376537084579468},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.6157577633857727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5863118767738342},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5699498653411865},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.5214221477508545},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5131345391273499},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33307939767837524},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1110493540763855},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08109891414642334},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2525314.2525465","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2525314.2525465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.716.77","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.716.77","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://web.ornl.gov/%7Ejiy/papers/YC_ACMSPATIAL13.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6299999952316284}],"awards":[{"id":"https://openalex.org/G1275972405","display_name":null,"funder_award_id":"DOE-NNSA/NA-22","funder_id":"https://openalex.org/F4320332369","funder_display_name":"National Nuclear Security Administration"}],"funders":[{"id":"https://openalex.org/F4320332369","display_name":"National Nuclear Security Administration","ror":"https://ror.org/03sk1we31"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W76086797","https://openalex.org/W2071091794","https://openalex.org/W2073015676","https://openalex.org/W2074739868","https://openalex.org/W2111308925","https://openalex.org/W2156531019","https://openalex.org/W2798909945"],"related_works":["https://openalex.org/W3162200841","https://openalex.org/W2586280620","https://openalex.org/W2805505483","https://openalex.org/W2334071950","https://openalex.org/W2384744344","https://openalex.org/W4233932308","https://openalex.org/W1799694159","https://openalex.org/W2393169196","https://openalex.org/W2366610330","https://openalex.org/W4242143973"],"abstract_inverted_index":{"The":[0,89,105],"trace":[1,40,82],"data":[2,22,54,127],"generated":[3],"from":[4,142],"GPS":[5,39,53,126,143],"enabled":[6],"vehicles":[7],"is":[8,61,73],"highly":[9],"valuable":[10],"for":[11,123],"applications":[12],"such":[13],"as":[14],"map":[15,140],"inference":[16,141],"and":[17,84,92,133],"traffic":[18],"analysis.":[19,41],"However,":[20],"the":[21,77,85,99,136],"tends":[23],"to":[24,28,55,75,96,110],"be":[25],"noisy":[26,125],"due":[27],"signal":[29],"interference.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34],"introduce":[35],"aerial":[36],"images":[37],"in":[38],"Computer":[42],"vision":[43],"techniques":[44],"are":[45,107],"developed":[46],"that":[47,117],"effectively":[48],"integrate":[49],"image":[50,60,87],"information":[51],"with":[52,103,128],"generate":[56],"road":[57,113],"networks.":[58,114],"An":[59],"first":[62],"segmented":[63],"by":[64],"an":[65],"efficient":[66],"factorization-based":[67],"algorithm.":[68],"A":[69],"structure":[70],"tensor":[71],"approach":[72],"proposed":[74],"measure":[76],"orientation":[78,93],"difference":[79],"between":[80],"a":[81,129],"segment":[83],"corresponding":[86],"patch.":[88],"segmentation":[90],"result":[91],"measures":[94],"lead":[95],"significantly":[97],"reducing":[98],"traces":[100,106],"not":[101],"aligning":[102],"roads.":[104],"further":[108],"processed":[109],"produce":[111],"high-quality":[112],"We":[115],"show":[116],"our":[118],"method":[119,138],"produces":[120],"promising":[121],"results":[122],"very":[124],"low":[130],"sampling":[131],"rate":[132],"also":[134],"outperforms":[135],"leading":[137],"of":[139],"traces.":[144]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}