{"id":"https://openalex.org/W4312847532","doi":"https://doi.org/10.1109/igarss46834.2022.9884323","title":"Burnt Area Segmentation with Densely Layered Capsules","display_name":"Burnt Area Segmentation with Densely Layered Capsules","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312847532","doi":"https://doi.org/10.1109/igarss46834.2022.9884323"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884323","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"article","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/A5084651957","display_name":"Qurratulain Safder","orcid":"https://orcid.org/0000-0003-0608-3129"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qurratulain Safder","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,Sichuan,PRC,611731"],"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,Sichuan,PRC,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425904","display_name":"Haoyu Zhang","orcid":"https://orcid.org/0009-0006-0459-9892"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyu Zhang","raw_affiliation_strings":["Glasgow College, University of Electronic Science and Technology of China,Chengdu,Sichuan,PRC,611731"],"affiliations":[{"raw_affiliation_string":"Glasgow College, University of Electronic Science and Technology of China,Chengdu,Sichuan,PRC,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083627327","display_name":"Zezhong Zheng","orcid":"https://orcid.org/0000-0002-5615-5015"},"institutions":[{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"funder","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zezhong Zheng","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,Sichuan,PRC,611731","Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen, Guangdong, PRC"],"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,Sichuan,PRC,611731","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen, Guangdong, PRC","institution_ids":["https://openalex.org/I211433327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084651957"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.9294,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.7311622,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2199","last_page":"2202"},"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9979000091552734,"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/T10535","display_name":"Landslides and related hazards","score":0.9743000268936157,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8006385564804077},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7346665859222412},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7255774140357971},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.719124436378479},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6518064737319946},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6278166174888611},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49710729718208313},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44635000824928284},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3830024302005768},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15105336904525757},{"id":"https://openalex.org/keywords/archaeology","display_name":"Archaeology","score":0.06192260980606079}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8006385564804077},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7346665859222412},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7255774140357971},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.719124436378479},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6518064737319946},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6278166174888611},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49710729718208313},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44635000824928284},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3830024302005768},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15105336904525757},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.06192260980606079}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884323","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6700000166893005,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G2804959498","display_name":null,"funder_award_id":"2020YFSY0041","funder_id":"https://openalex.org/F4320335774","funder_display_name":"Key Technologies Research and Development Program"}],"funders":[{"id":"https://openalex.org/F4320335774","display_name":"Key Technologies Research and Development Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2017926030","https://openalex.org/W2065588012","https://openalex.org/W2099964454","https://openalex.org/W2112186536","https://openalex.org/W2165794527","https://openalex.org/W2735654840","https://openalex.org/W2797472209","https://openalex.org/W2963703618","https://openalex.org/W2975479989","https://openalex.org/W3003452346","https://openalex.org/W3153568248","https://openalex.org/W3205657035","https://openalex.org/W6743446608"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2810679507","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"In":[0],"the":[1,18,27,60,73,96,110],"presented":[2,47],"article,":[3],"deep":[4],"learning":[5],"(DL)":[6],"is":[7,26,49,84],"employed":[8],"on":[9],"Sentinel-2":[10],"and":[11,16,43,55,127],"Landsat":[12],"imagery":[13],"to":[14,37,72,131],"assess":[15],"delineate":[17],"wildfire-affected":[19],"burnt":[20,56,97],"area.":[21],"The":[22,46,89,105,121],"most":[23],"important":[24],"aspect":[25],"use":[28],"of":[29,67,75,95,103],"densely":[30],"layered":[31],"capsule":[32],"architecture":[33,113,123],"as":[34],"an":[35,100],"alternative":[36],"traditional":[38],"convolutional":[39],"neural":[40],"networks":[41],"(CNN)":[42],"max":[44],"pooling.":[45],"investigation":[48],"a":[50,64,82],"practical":[51],"capsule-based":[52,68,112,122],"wildfire":[53],"detection":[54],"area":[57,98],"segmentation.":[58],"Moreover,":[59],"research":[61],"work":[62],"presents":[63],"comprehensive":[65],"assessment":[66],"segmentation":[69,117],"architecture.":[70],"Due":[71],"limitation":[74],"freely":[76],"available":[77],"open":[78],"access":[79],"annotated":[80],"data,":[81],"dataset":[83],"manually":[85],"constructed":[86],"from":[87],"scratch.":[88],"proposed":[90,111],"algorithm":[91],"provides":[92],"prognostic":[93],"delineation":[94],"with":[99],"overall":[101],"accuracy":[102],"96%.":[104],"quantitative":[106],"measures":[107],"demonstrate":[108],"that":[109],"outperforms":[114],"other":[115,132],"state-of-the-art":[116],"models":[118],"(U-Net,":[119],"DeepLabV3+).":[120],"shows":[124],"good":[125],"promise":[126],"can":[128],"be":[129],"scaled":[130],"similar":[133],"topographic":[134],"regions.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}