{"id":"https://openalex.org/W4392982010","doi":"https://doi.org/10.1109/cvmi59935.2023.10464880","title":"Post Flood Damage Assessment Using Aerial Imagery","display_name":"Post Flood Damage Assessment Using Aerial Imagery","publication_year":2023,"publication_date":"2023-12-10","ids":{"openalex":"https://openalex.org/W4392982010","doi":"https://doi.org/10.1109/cvmi59935.2023.10464880"},"language":"en","primary_location":{"id":"doi:10.1109/cvmi59935.2023.10464880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi59935.2023.10464880","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","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/A5094198854","display_name":"Tarun Dixit","orcid":null},"institutions":[{"id":"https://openalex.org/I9747756","display_name":"Atal Bihari Vajpayee Indian Institute of Information Technology and Management","ror":"https://ror.org/008b3ap06","country_code":"IN","type":"education","lineage":["https://openalex.org/I9747756"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Tarun Dixit","raw_affiliation_strings":["ABV-Indian Institute of Information Technology and Management,Computer Science and Engineering,Gwalior,India","Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management, Gwalior, India"],"affiliations":[{"raw_affiliation_string":"ABV-Indian Institute of Information Technology and Management,Computer Science and Engineering,Gwalior,India","institution_ids":["https://openalex.org/I9747756"]},{"raw_affiliation_string":"Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management, Gwalior, India","institution_ids":["https://openalex.org/I9747756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023471512","display_name":"Pramod Kumar Singh","orcid":"https://orcid.org/0000-0003-0019-1436"},"institutions":[{"id":"https://openalex.org/I9747756","display_name":"Atal Bihari Vajpayee Indian Institute of Information Technology and Management","ror":"https://ror.org/008b3ap06","country_code":"IN","type":"education","lineage":["https://openalex.org/I9747756"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pramod Kumar Singh","raw_affiliation_strings":["ABV-Indian Institute of Information Technology and Management,Computer Science and Engineering,Gwalior,India","Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management, Gwalior, India"],"affiliations":[{"raw_affiliation_string":"ABV-Indian Institute of Information Technology and Management,Computer Science and Engineering,Gwalior,India","institution_ids":["https://openalex.org/I9747756"]},{"raw_affiliation_string":"Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management, Gwalior, India","institution_ids":["https://openalex.org/I9747756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5094198854"],"corresponding_institution_ids":["https://openalex.org/I9747756"],"apc_list":null,"apc_paid":null,"fwci":0.1259,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46897745,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9086999893188477,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9086999893188477,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/flood-myth","display_name":"Flood myth","score":0.7692468166351318},{"id":"https://openalex.org/keywords/aerial-imagery","display_name":"Aerial imagery","score":0.5863868594169617},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5184412002563477},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48739463090896606},{"id":"https://openalex.org/keywords/aerial-photos","display_name":"Aerial photos","score":0.4575038254261017},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.38805896043777466},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3522026538848877},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34774085879325867},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.16257843375205994},{"id":"https://openalex.org/keywords/archaeology","display_name":"Archaeology","score":0.09932252764701843}],"concepts":[{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.7692468166351318},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.5863868594169617},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5184412002563477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48739463090896606},{"id":"https://openalex.org/C3018193623","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial photos","level":2,"score":0.4575038254261017},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.38805896043777466},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3522026538848877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34774085879325867},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.16257843375205994},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.09932252764701843}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvmi59935.2023.10464880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi59935.2023.10464880","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1159302035","https://openalex.org/W1479807131","https://openalex.org/W1535753778","https://openalex.org/W1901129140","https://openalex.org/W2007339694","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2283626220","https://openalex.org/W2395611524","https://openalex.org/W2417429787","https://openalex.org/W2467999252","https://openalex.org/W2531409750","https://openalex.org/W2560023338","https://openalex.org/W2630837129","https://openalex.org/W2787091153","https://openalex.org/W2896446634","https://openalex.org/W2901139858","https://openalex.org/W2924082650","https://openalex.org/W2953070460","https://openalex.org/W2963526604","https://openalex.org/W2963956526","https://openalex.org/W2964159205","https://openalex.org/W2978426779","https://openalex.org/W2982083293","https://openalex.org/W3001197829","https://openalex.org/W3014606712","https://openalex.org/W3027938990","https://openalex.org/W3035160371","https://openalex.org/W3118608800","https://openalex.org/W3148181069","https://openalex.org/W3175205795","https://openalex.org/W3211490618","https://openalex.org/W4205569865","https://openalex.org/W4205806420","https://openalex.org/W4300273322","https://openalex.org/W6733814495","https://openalex.org/W6739696289","https://openalex.org/W6749396741","https://openalex.org/W6762913911","https://openalex.org/W6764051988","https://openalex.org/W6773005947","https://openalex.org/W6787972765","https://openalex.org/W6797399245"],"related_works":["https://openalex.org/W3080225444","https://openalex.org/W2784132289","https://openalex.org/W3208038162","https://openalex.org/W3162340925","https://openalex.org/W4251065912","https://openalex.org/W2075321446","https://openalex.org/W3205848368","https://openalex.org/W4226389740","https://openalex.org/W2991153303","https://openalex.org/W2889202529"],"abstract_inverted_index":{"Visual":[0],"Scene":[1],"understanding":[2],"is":[3,30,92],"one":[4,31],"of":[5,32,70,207,224],"the":[6,12,40,68,75,83,103,118,133,156,181],"most":[7],"efficient":[8],"methods":[9],"to":[10,38,60,82,116,145,167],"assess":[11,39],"damage":[13,41],"caused":[14],"by":[15,43,148],"natural":[16],"disasters.":[17],"Many":[18],"datasets":[19,34],"exist":[20],"that":[21],"contain":[22],"aerial":[23,90],"images":[24,36,147,153],"from":[25,105,151],"satellites":[26],"or":[27],"UAVs.":[28],"Floodnet":[29,76],"those":[33],"containing":[35],"useful":[37],"done":[42],"a":[44,112,176,200,217],"flood.":[45],"Different":[46],"network":[47],"architectures":[48],"have":[49],"been":[50,130],"developed":[51],"for":[52,192],"classification":[53,119,137],"and":[54,95,120,138,205,209],"semantic":[55,121,139],"segmentation":[56,122,140],"using":[57],"supervised":[58],"learning":[59,114],"achieve":[61,168],"reasonable":[62],"accuracy.":[63,108],"However,":[64],"they":[65],"cannot":[66],"address":[67,117],"problem":[69],"considerable":[71],"class":[72],"imbalance":[73,79,99],"in":[74,100,136,175,199],"dataset.":[77],"This":[78,98,109],"arises":[80],"due":[81],"few":[84],"labeled":[85,152],"images,":[86],"as":[87],"labeling":[88],"an":[89,93],"image":[91],"expensive":[94],"high-effort":[96],"task.":[97],"classes":[101,150],"prevents":[102],"model":[104,135,196,214],"achieving":[106],"good":[107],"work":[110],"uses":[111],"semi-supervised":[113,177,201],"approach":[115,128],"problem.":[123],"The":[124],"classical":[125],"Pseudo":[126],"Labeling":[127],"has":[129],"implemented":[131],"on":[132,155,180],"best-performing":[134],"objectives.":[141],"Pseudo-labels":[142],"are":[143,161],"assigned":[144],"unlabeled":[146],"predicting":[149],"based":[154,179],"probability":[157],"distribution.":[158],"These":[159],"pseudo-labels":[160],"then":[162],"trained":[163,198],"with":[164],"ground-truth":[165],"data":[166],"better":[169],"results.":[170],"Classification":[171,195],"models":[172,187],"train":[173],"themselves":[174],"manner":[178,202],"pseudo-labels'":[182],"generation":[183],"algorithm.":[184],"Semantic":[185,212],"Segmentation":[186,213],"also":[188],"generate":[189],"pseudo-label":[190],"masks":[191],"pixel-level":[193],"identification.":[194],"Resnet50":[197],"achieves":[203,216],"accuracy":[204],"Fl-score":[206],"94.97%":[208],"0.971,":[210],"respectively.":[211],"Segformer":[215],"Mean":[218],"Intersection":[219],"over":[220],"Union":[221],"(MIoU)":[222],"score":[223],"0.596.":[225]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}