{"id":"https://openalex.org/W3021622360","doi":"https://doi.org/10.1109/icoac48765.2019.246817","title":"Deep Learning Based Forest Fire Classification and Detection in Satellite Images","display_name":"Deep Learning Based Forest Fire Classification and Detection in Satellite Images","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3021622360","doi":"https://doi.org/10.1109/icoac48765.2019.246817","mag":"3021622360"},"language":"en","primary_location":{"id":"doi:10.1109/icoac48765.2019.246817","doi":"https://doi.org/10.1109/icoac48765.2019.246817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icoac48765.2019.246817","pdf_url":null,"source":{"id":"https://openalex.org/S4306419096","display_name":"International Conference on Advanced Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 11th International Conference on Advanced Computing (ICoAC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5100636179","display_name":"R. Shanmuga Priya","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"R. Shanmuga priya","raw_affiliation_strings":["Department of Information Science and Technology, Guindy Anna University, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Science and Technology, Guindy Anna University, Chennai, India","institution_ids":["https://openalex.org/I33585257"]}]},{"author_position":"last","author":{"id":null,"display_name":"K. Vani","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K. Vani","raw_affiliation_strings":["Department of Information Science and Technology, Guindy Anna University, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Science and Technology, Guindy Anna University, Chennai, India","institution_ids":["https://openalex.org/I33585257"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I33585257"],"apc_list":null,"apc_paid":null,"fwci":15.0332,"has_fulltext":false,"cited_by_count":128,"citation_normalized_percentile":{"value":0.99566673,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"61","last_page":"65"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.0003000000142492354,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"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/wildfire-detection","display_name":"forest fire detection","score":0.996999979019165},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"satellite imagery","score":0.9240000247955322},{"id":"https://openalex.org/keywords/inception-v3","display_name":"InceptionV3","score":0.9539999961853027},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.9990000128746033},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9179999828338623},{"id":"https://openalex.org/keywords/smoke-detection","display_name":"smoke detection","score":0.8180000185966492},{"id":"https://openalex.org/keywords/local-binary-pattern","display_name":"local binary pattern","score":0.8019999861717224},{"id":"https://openalex.org/keywords/false-positive-reduction","display_name":"false positive reduction","score":0.8790000081062317}],"concepts":[{"id":"https://openalex.org/C2780836893","wikidata":"https://www.wikidata.org/wiki/Q19922674","display_name":"Fire detection","level":2,"score":0.7200955748558044},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6568456888198853},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6041297912597656},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.571003794670105},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.5403338670730591},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.515428900718689},{"id":"https://openalex.org/C58874564","wikidata":"https://www.wikidata.org/wiki/Q130768","display_name":"Smoke","level":2,"score":0.48094457387924194},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4674445390701294},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.46662816405296326},{"id":"https://openalex.org/C138602881","wikidata":"https://www.wikidata.org/wiki/Q2709591","display_name":"Confusion matrix","level":2,"score":0.43484586477279663},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4176592528820038},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4122597873210907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38713356852531433},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.19586148858070374},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.17536050081253052},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.12843862175941467},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11894881725311279},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icoac48765.2019.246817","doi":"https://doi.org/10.1109/icoac48765.2019.246817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icoac48765.2019.246817","pdf_url":null,"source":{"id":"https://openalex.org/S4306419096","display_name":"International Conference on Advanced Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 11th International Conference on Advanced Computing (ICoAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7605999708175659,"display_name":"Life on land","id":"https://metadata.un.org/sdg/15"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W646984897","https://openalex.org/W1909366555","https://openalex.org/W2072458323","https://openalex.org/W2282689446","https://openalex.org/W2402312219","https://openalex.org/W2571300184","https://openalex.org/W2614024545","https://openalex.org/W2741119099","https://openalex.org/W2793947836","https://openalex.org/W2803990755","https://openalex.org/W2807862495","https://openalex.org/W2941454446","https://openalex.org/W3105270602"],"related_works":["https://openalex.org/W4327573076","https://openalex.org/W4378448051","https://openalex.org/W4378588486","https://openalex.org/W4400977169","https://openalex.org/W7212138927","https://openalex.org/W4382052431","https://openalex.org/W7126165900","https://openalex.org/W4380884997","https://openalex.org/W4322731423","https://openalex.org/W4377700172"],"abstract_inverted_index":{"Forest":[0],"fires":[1],"are":[2],"an":[3,76],"important":[4],"threat":[5],"to":[6,56,111],"humans":[7],"and":[8,23,50,97,104],"other":[9,51],"living":[10],"creatures,":[11],"with":[12],"the":[13,29,32,94,99,115,119,124],"development":[14],"of":[15,26,34,79,114],"satellite":[16,95,125],"technology":[17],"it":[18,131],"can":[19],"be":[20],"constantly":[21],"monitored":[22],"controlled.":[24],"Presence":[25],"smoke":[27],"in":[28,47,123],"atmosphere":[30],"is":[31,69,90,109],"indication":[33],"forest":[35],"wildfires.":[36],"In":[37],"fire":[38,41,52,64,73,103,120],"alarm":[39],"systems,":[40],"detection":[42,65,74,134],"plays":[43],"a":[44,80,102],"crucial":[45],"part":[46],"avoiding":[48],"damages":[49],"disasters":[53],"that":[54],"lead":[55],"social":[57],"ramifications.":[58],"Avoiding":[59],"large":[60],"scale":[61],"fire,":[62],"effective":[63,77],"from":[66],"visual":[67],"scenes":[68],"important.":[70],"To":[71],"improve":[72],"accuracy,":[75],"approach":[78],"convolutional":[81],"neural":[82],"network":[83],"based":[84,86],"Inception-v3":[85],"on":[87],"transfer":[88],"learning":[89],"designed":[91],"which":[92],"train":[93],"images":[96],"classify":[98],"datasets":[100],"into":[101],"non-fire":[105],"images,":[106],"confusion":[107],"matrix":[108],"generated":[110],"specify":[112],"efficiency":[113],"framework,":[116],"then":[117],"extract":[118],"occurred":[121],"region":[122],"image":[126],"using":[127],"local":[128],"binary":[129],"pattern":[130],"reduces":[132],"false":[133],"rates.":[135]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":56},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":6}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2020-05-13T00:00:00"}
