{"id":"https://openalex.org/W7147737504","doi":"https://doi.org/10.48550/arxiv.2603.27486","title":"Estimating the Impact of COVID-19 on Travel Demand in Houston Area Using Deep Learning and Satellite Imagery","display_name":"Estimating the Impact of COVID-19 on Travel Demand in Houston Area Using Deep Learning and Satellite Imagery","publication_year":2026,"publication_date":"2026-03-29","ids":{"openalex":"https://openalex.org/W7147737504","doi":"https://doi.org/10.48550/arxiv.2603.27486"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27486","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27486","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.27486","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092156195","display_name":"Alekhya Pachika","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pachika, Alekhya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132602627","display_name":"Lu Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111456873","display_name":"L Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Lingguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132551293","display_name":"Pan Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Pan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132655537","display_name":"Xingju Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xingju","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.4603999853134155,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.4603999853134155,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":0.09390000253915787,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T10410","display_name":"COVID-19 epidemiological studies","score":0.0544000007212162,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.7840999960899353},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.6897000074386597},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6671000123023987},{"id":"https://openalex.org/keywords/metropolitan-area","display_name":"Metropolitan area","score":0.6467999815940857},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.3440999984741211},{"id":"https://openalex.org/keywords/geographic-information-system","display_name":"Geographic information system","score":0.323199987411499}],"concepts":[{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.7840999960899353},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.6897000074386597},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6671000123023987},{"id":"https://openalex.org/C158739034","wikidata":"https://www.wikidata.org/wiki/Q1907114","display_name":"Metropolitan area","level":2,"score":0.6467999815940857},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6317999958992004},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44600000977516174},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3540000021457672},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.3319000005722046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3287000060081482},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2669000029563904},{"id":"https://openalex.org/C2985733770","wikidata":"https://www.wikidata.org/wiki/Q1233007","display_name":"Travel time","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.257099986076355}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27486","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27486","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.27486","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27486","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6321349740028381}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Considering":[0],"recent":[1],"advances":[2],"in":[3,91,154],"remote":[4],"sensing":[5,14],"satellite":[6,13,59,70,98,167],"systems":[7],"and":[8,16,25,77,112,133,175,184,192],"computer":[9,182],"vision":[10,183],"algorithms,":[11,187],"many":[12],"platforms":[15],"sensors":[17],"have":[18],"been":[19],"used":[20],"to":[21,72,115],"monitor":[22,116],"the":[23,42,87,92,117,135,141,158],"condition":[24],"usage":[26],"of":[27,33,44,89,95,119,143],"transportation":[28,196],"infrastructure":[29],"systems.":[30],"The":[31,137,162],"level":[32],"details":[34],"that":[35,140,166],"can":[36,189],"be":[37],"detected":[38,145],"increases":[39],"significantly":[40],"with":[41,157,180],"increase":[43],"ground":[45],"sample":[46],"distance":[47],"(GSD),":[48],"which":[49],"is":[50],"around":[51],"15":[52],"cm":[53,56],"-":[54],"30":[55],"for":[57,79,172,195],"high-resolution":[58,69],"images.":[60],"In":[61],"this":[62],"study,":[63],"we":[64,85],"analyzed":[65],"data":[66],"acquired":[67],"from":[68,100],"imagery":[71,99,168],"provide":[73],"insights,":[74],"predictive":[75],"signals,":[76],"trend":[78],"travel":[80,173],"demand":[81,174],"estimation.":[82,178],"More":[83],"specifically,":[84],"estimate":[86],"impact":[88],"COVID-19":[90],"metropolitan":[93],"area":[94],"Houston":[96],"using":[97],"Google":[101],"Earth":[102],"Engine":[103],"datasets.":[104],"We":[105],"developed":[106],"a":[107],"car-counting":[108],"model":[109],"through":[110],"Detectron2":[111],"Faster":[113],"R-CNN":[114],"presence":[118],"cars":[120,144],"within":[121],"different":[122],"locations":[123,149],"(i.e.,":[124],"university,":[125],"shopping":[126],"mall,":[127],"community":[128],"plaza,":[129],"restaurant,":[130],"supermarket)":[131],"before":[132],"during":[134],"COVID-19.":[136],"results":[138,163],"show":[139,165],"number":[142],"at":[146],"these":[147],"selected":[148],"reduced":[150],"on":[151],"average":[152],"30%":[153],"2020":[155],"compared":[156],"previous":[159],"year":[160],"2019.":[161],"also":[164],"provides":[169],"rich":[170],"information":[171,194],"economic":[176],"activity":[177],"Together":[179],"advanced":[181],"deep":[185],"learning":[186],"it":[188],"generate":[190],"reliable":[191],"accurate":[193],"agency":[197],"decision":[198],"makers.":[199]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
