{"id":"https://openalex.org/W7133508522","doi":"https://doi.org/10.48550/arxiv.2603.02465","title":"Deep Learning Based Wildfire Detection for Peatland Fires Using Transfer Learning","display_name":"Deep Learning Based Wildfire Detection for Peatland Fires Using Transfer Learning","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133508522","doi":"https://doi.org/10.48550/arxiv.2603.02465"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.02465","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02465","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.2603.02465","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128120447","display_name":"Emadeldeen Hamdan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hamdan, Emadeldeen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068403585","display_name":"Ahmad Faiz Tharima","orcid":"https://orcid.org/0000-0003-2624-2254"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tharima, Ahmad Faiz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094310012","display_name":"MOHD ZAHIRASRI MOHD TOHIR","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tohir, Mohd Zahirasri Mohd","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049766019","display_name":"Dayang Nur Sakinah Musa","orcid":"https://orcid.org/0000-0002-5330-8362"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Musa, Dayang Nur Sakinah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046275838","display_name":"Erdem Koyuncu","orcid":"https://orcid.org/0000-0002-6238-0470"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koyuncu, Erdem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125989422","display_name":"Adam Watts","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Watts, Adam J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128061213","display_name":"Ahmet Enis Cetin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cetin, Ahmet Enis","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/T12597","display_name":"Fire Detection and Safety Systems","score":0.654699981212616,"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":0.654699981212616,"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/T10555","display_name":"Fire effects on ecosystems","score":0.31700000166893005,"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/T11019","display_name":"Image Enhancement Techniques","score":0.004000000189989805,"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/peat","display_name":"Peat","score":0.8313999772071838},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6089000105857849},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5849999785423279},{"id":"https://openalex.org/keywords/fire-detection","display_name":"Fire detection","score":0.5508000254631042},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5037000179290771},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.36880001425743103}],"concepts":[{"id":"https://openalex.org/C53657456","wikidata":"https://www.wikidata.org/wiki/Q184624","display_name":"Peat","level":2,"score":0.8313999772071838},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6089000105857849},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5849999785423279},{"id":"https://openalex.org/C2780836893","wikidata":"https://www.wikidata.org/wiki/Q19922674","display_name":"Fire detection","level":2,"score":0.5508000254631042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5467000007629395},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5353999733924866},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5320000052452087},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5037000179290771},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4422999918460846},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43160000443458557},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26600000262260437},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.02465","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02465","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.2603.02465","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02465","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":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.5023282170295715}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning":[1,15,127,142],"(ML)-based":[2],"wildfire":[3,23,56,82,105],"detection":[4,75,106,145,180],"methods":[5],"have":[6],"been":[7],"developed":[8],"in":[9],"recent":[10],"years,":[11],"primarily":[12],"using":[13,99,113],"deep":[14],"(DL)":[16],"models":[17],"trained":[18,58],"on":[19,59],"large":[20],"collections":[21],"of":[22,54,117,132],"images":[24,120],"and":[25,33,46,84,108,121,147,164,173,181,193],"videos.":[26,122],"However,":[27],"peatland":[28,73,89,96,119,134,178],"fires":[29],"exhibit":[30],"distinct":[31],"visual":[32],"physical":[34],"characteristics":[35],"--":[36,49],"such":[37,158],"as":[38,159],"smoldering":[39],"combustion,":[40],"low":[41],"flame":[42],"intensity,":[43],"persistent":[44],"smoke,":[45,161],"subsurface":[47],"burning":[48],"that":[50,76,140],"limit":[51],"the":[52,88,111,129,183],"effectiveness":[53],"conventional":[55,104],"detectors":[57],"open-flame":[60],"forest":[61],"fires.":[62],"In":[63],"this":[64],"work,":[65],"we":[66],"present":[67],"a":[68,94,103,114,171],"transfer":[69,141],"learning-based":[70],"approach":[71,169],"for":[72,176,190],"fire":[74,90,97,135,179,191],"leverages":[77],"knowledge":[78],"learned":[79],"from":[80,102,152],"general":[81],"imagery":[83],"adapts":[85],"it":[86],"to":[87,150,185],"domain.":[91],"We":[92],"initialize":[93],"DL-based":[95],"detector":[98],"pretrained":[100],"weights":[101],"model":[107],"subsequently":[109],"fine-tune":[110],"network":[112],"dataset":[115],"composed":[116],"Malaysian":[118],"This":[123],"strategy":[124],"enables":[125],"effective":[126],"despite":[128],"limited":[130],"availability":[131],"labeled":[133],"data.":[136],"Experimental":[137],"results":[138],"demonstrate":[139],"significantly":[143],"improves":[144],"accuracy":[146],"robustness":[148],"compared":[149],"training":[151],"scratch,":[153],"particularly":[154],"under":[155],"challenging":[156],"conditions":[157],"low-contrast":[160],"partial":[162],"occlusions,":[163],"variable":[165],"illumination.":[166],"The":[167],"proposed":[168],"provides":[170],"practical":[172],"scalable":[174],"solution":[175],"early":[177],"has":[182],"potential":[184],"support":[186],"real-time":[187],"monitoring":[188],"systems":[189],"prevention":[192],"environmental":[194],"protection.":[195]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-05T00:00:00"}