{"id":"https://openalex.org/W4225363088","doi":"https://doi.org/10.1109/tgrs.2022.3192974","title":"Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data","display_name":"Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4225363088","doi":"https://doi.org/10.1109/tgrs.2022.3192974"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3192974","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3192974","pdf_url":"https://ieeexplore.ieee.org/ielx7/36/4358825/09840400.pdf","source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/36/4358825/09840400.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080734082","display_name":"Fantine Huot","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fantine Huot","raw_affiliation_strings":["Research Department, Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Research Department, Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051725480","display_name":"R. Lily Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R. Lily Hu","raw_affiliation_strings":["Research Department, Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Research Department, Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090909284","display_name":"Nita Goyal","orcid":"https://orcid.org/0000-0001-9087-9976"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nita Goyal","raw_affiliation_strings":["Research Department, Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Research Department, Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000116008","display_name":"Tharun Sankar","orcid":"https://orcid.org/0000-0002-0557-0972"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tharun Sankar","raw_affiliation_strings":["Research Department, Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Research Department, Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057929380","display_name":"Matthias Ihme","orcid":"https://orcid.org/0000-0002-4158-7050"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthias Ihme","raw_affiliation_strings":["Research Department, Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Research Department, Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100405161","display_name":"Yifan Chen","orcid":"https://orcid.org/0009-0007-0048-7751"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi-Fan Chen","raw_affiliation_strings":["Research Department, Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Research Department, Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5080734082"],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":11.4082,"has_fulltext":true,"cited_by_count":107,"citation_normalized_percentile":{"value":0.99216105,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":1.0,"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/T10535","display_name":"Landslides and related hazards","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5842023491859436},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5386338829994202},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5202921032905579},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5097813010215759},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4770183563232422},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45051565766334534},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.419842392206192},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.416995108127594},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4038664698600769},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.370769202709198},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2720690965652466},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.23645544052124023}],"concepts":[{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5842023491859436},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5386338829994202},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5202921032905579},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5097813010215759},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4770183563232422},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45051565766334534},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.419842392206192},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.416995108127594},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4038664698600769},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.370769202709198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2720690965652466},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.23645544052124023},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2022.3192974","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3192974","pdf_url":"https://ieeexplore.ieee.org/ielx7/36/4358825/09840400.pdf","source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2112.02447","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2112.02447","pdf_url":"https://arxiv.org/pdf/2112.02447","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1109/tgrs.2022.3192974","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3192974","pdf_url":"https://ieeexplore.ieee.org/ielx7/36/4358825/09840400.pdf","source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4225363088.pdf","grobid_xml":"https://content.openalex.org/works/W4225363088.grobid-xml"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W871842247","https://openalex.org/W1526406472","https://openalex.org/W1565038399","https://openalex.org/W1951684732","https://openalex.org/W1965599912","https://openalex.org/W1977004800","https://openalex.org/W1995047010","https://openalex.org/W1998525406","https://openalex.org/W2000353051","https://openalex.org/W2060959422","https://openalex.org/W2067559655","https://openalex.org/W2070151867","https://openalex.org/W2084744129","https://openalex.org/W2085696766","https://openalex.org/W2097086408","https://openalex.org/W2098480391","https://openalex.org/W2099831943","https://openalex.org/W2101234009","https://openalex.org/W2102467070","https://openalex.org/W2108528697","https://openalex.org/W2117539524","https://openalex.org/W2123350263","https://openalex.org/W2125408438","https://openalex.org/W2145445755","https://openalex.org/W2158439599","https://openalex.org/W2165977355","https://openalex.org/W2171102977","https://openalex.org/W2271840356","https://openalex.org/W2294741226","https://openalex.org/W2395021472","https://openalex.org/W2484421982","https://openalex.org/W2494302106","https://openalex.org/W2557002900","https://openalex.org/W2573821892","https://openalex.org/W2593972424","https://openalex.org/W2725897987","https://openalex.org/W2747246859","https://openalex.org/W2847765000","https://openalex.org/W2890777951","https://openalex.org/W2903942159","https://openalex.org/W2910187721","https://openalex.org/W2913649977","https://openalex.org/W2925105068","https://openalex.org/W2964040809","https://openalex.org/W2965595054","https://openalex.org/W2990090364","https://openalex.org/W2991126022","https://openalex.org/W3008626511","https://openalex.org/W3092943061","https://openalex.org/W3098105755","https://openalex.org/W3099079911","https://openalex.org/W3104803445","https://openalex.org/W3126677426","https://openalex.org/W4255549514","https://openalex.org/W6605427221","https://openalex.org/W6675354045","https://openalex.org/W6694517276","https://openalex.org/W6703949738","https://openalex.org/W6784398874","https://openalex.org/W6892256511","https://openalex.org/W6929465847"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W4246352526","https://openalex.org/W3114272811"],"abstract_inverted_index":{"Predicting":[0],"wildfire":[1,108,139],"spread":[2],"is":[3],"critical":[4],"for":[5,80,137,147],"land":[6],"management":[7],"and":[8,125],"disaster":[9],"preparedness.":[10],"To":[11,83],"this":[12,88,104],"end,":[13],"we":[14,91],"present":[15],"’Next":[16],"Day":[17],"Wildfire":[18],"Spread,’":[19],"a":[20,31,76,93,135,148],"curated,":[21],"large-scale,":[22],"multivariate":[23],"data":[24,35,45,53,58,78,89,105,129,146],"set":[25,54,79,130],"of":[26,33,87,99,103,114,151],"historical":[27],"wildfires":[28],"aggregating":[29],"nearly":[30],"decade":[32],"remote-sensing":[34],"across":[36],"the":[37,85,100,112,115],"United":[38],"States.":[39],"In":[40],"contrast":[41],"to":[42,106],"existing":[43],"fire":[44,57],"sets":[46],"based":[47,142],"on":[48,143],"Earth":[49],"observation":[50],"satellites,":[51],"our":[52],"combines":[55],"2D":[56,73],"with":[59,118],"multiple":[60],"explanatory":[61],"variables":[62],"(e.g.,":[63],"topography,":[64],"vegetation,":[65],"weather,":[66],"drought":[67],"index,":[68],"population":[69],"density)":[70],"aligned":[71],"over":[72],"regions,":[74],"providing":[75],"feature-rich":[77],"machine":[81,120],"learning.":[82],"demonstrate":[84],"usefulness":[86],"set,":[90],"implement":[92],"neural":[94,116],"network":[95,117],"that":[96],"takes":[97],"advantage":[98],"spatial":[101],"information":[102],"predict":[107],"spread.":[109],"We":[110],"compare":[111],"performance":[113],"other":[119],"learning":[121],"models:":[122],"logistic":[123],"regression":[124],"random":[126],"forest.":[127],"This":[128],"can":[131],"be":[132],"used":[133],"as":[134],"benchmark":[136],"developing":[138],"propagation":[140],"models":[141],"remote":[144],"sensing":[145],"lead":[149],"time":[150],"one":[152],"day.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":40},{"year":2024,"cited_by_count":34},{"year":2023,"cited_by_count":24},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2022-05-05T00:00:00"}