{"id":"https://openalex.org/W2983022339","doi":"https://doi.org/10.1109/igarss.2019.8900250","title":"Multi-Class Segmentation of Urban Floods from Multispectral Imagery Using Deep Learning","display_name":"Multi-Class Segmentation of Urban Floods from Multispectral Imagery Using Deep Learning","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2983022339","doi":"https://doi.org/10.1109/igarss.2019.8900250","mag":"2983022339"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8900250","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900250","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 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/A5089748744","display_name":"Abhishek Potnis","orcid":"https://orcid.org/0000-0001-8168-857X"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abhishek V. Potnis","raw_affiliation_strings":["Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056007385","display_name":"Rajat C. Shinde","orcid":"https://orcid.org/0000-0002-9505-6204"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajat C. Shinde","raw_affiliation_strings":["Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052217638","display_name":"Surya S. Durbha","orcid":"https://orcid.org/0000-0003-1022-8378"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Surya S. Durbha","raw_affiliation_strings":["Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025725109","display_name":"Kuldeep Kurte","orcid":"https://orcid.org/0000-0001-5797-7654"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kuldeep R. Kurte","raw_affiliation_strings":["Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India","institution_ids":["https://openalex.org/I162827531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162827531"],"apc_list":null,"apc_paid":null,"fwci":1.2685,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.83425163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"9741","last_page":"9744"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9911999702453613,"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"}},{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9908000230789185,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.8493186831474304},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6422098875045776},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6277272701263428},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6274154186248779},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5940168499946594},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.537832498550415},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4598046839237213},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4408869743347168},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3816218972206116},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.25688987970352173}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8493186831474304},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6422098875045776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6277272701263428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6274154186248779},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5940168499946594},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.537832498550415},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4598046839237213},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4408869743347168},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3816218972206116},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.25688987970352173}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8900250","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900250","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6200000047683716,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1910657905","https://openalex.org/W2170535121","https://openalex.org/W2412782625","https://openalex.org/W2538244214","https://openalex.org/W2618530766","https://openalex.org/W2740036778","https://openalex.org/W2762439315","https://openalex.org/W2964350391","https://openalex.org/W6639780620","https://openalex.org/W6694260854","https://openalex.org/W6715287400"],"related_works":["https://openalex.org/W4318664220","https://openalex.org/W2771047279","https://openalex.org/W4388409104","https://openalex.org/W2124951708","https://openalex.org/W1544811710","https://openalex.org/W172072032","https://openalex.org/W2006066416","https://openalex.org/W3157073418","https://openalex.org/W2058127401","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Natural":[0],"disasters":[1,24],"such":[2,25],"as":[3,26,38],"floods,":[4,206],"earthquakes,":[5],"hurricanes,":[6],"etc.":[7],"have":[8,140],"a":[9,13,111,120],"huge":[10],"impact":[11],"on":[12,68,88,119,185],"society-causing":[14],"destruction":[15],"of":[16,35,51,77,108,115,132,156,204],"life":[17],"and":[18,91,103,199],"property":[19],"in":[20,134,212],"their":[21],"wake.":[22],"During":[23],"flood,":[27],"it":[28,39],"is":[29,107,126],"crucial":[30],"to":[31,95,152],"understand":[32],"the":[33,36,49,69,97,113,116,193],"dynamics":[34],"situation":[37],"occurs":[40],"for":[41,55,74,143,164,201],"effective":[42],"response.":[43],"In":[44],"this":[45,144,165],"paper,":[46],"we":[47],"address":[48],"problem":[50],"satellite":[52,82,157,180],"image":[53],"classification":[54,203],"urban":[56,78,160,178,205],"floods":[57,79,133],"using":[58,170],"deep":[59,174],"learning.":[60],"We":[61,190],"propose":[62],"an":[63],"encoder-decoder":[64],"neural":[65],"network":[66],"based":[67],"Efficient":[70],"Residual":[71],"Factorized":[72],"Convnet(ERFNet),":[73],"multi-class":[75],"segmentation":[76],"from":[80,130],"multi-spectral":[81],"imagery.":[83,158],"The":[84,146,159,172],"ERFNet":[85,117],"architecture":[86,118],"capitalizes":[87],"skip":[89],"connections":[90],"one":[92],"dimensional":[93],"convolutions":[94],"achieve":[96],"best":[98],"possible":[99],"trade-off":[100],"between":[101],"accuracy":[102],"efficiency.":[104],"Since":[105],"time":[106],"essence":[109],"during":[110,137],"disaster,":[112],"choice":[114],"high":[121],"performance":[122],"computing":[123],"(HPC)":[124],"platform":[125],"apt.":[127],"Satellite":[128],"imagery":[129,181],"WorldView-2":[131],"Srinagar,":[135],"India":[136],"September":[138],"2014":[139],"been":[141,150,168],"used":[142,163],"study.":[145],"tool":[147],"`markGT'":[148],"has":[149,167],"developed":[151],"assist":[153],"end-to-end":[154],"annotation":[155],"flood":[161,179],"dataset":[162],"study":[166],"generated":[169],"markGT.":[171],"proposed":[173,194],"learning":[175],"model":[176,195],"over":[177],"gives":[182],"promising":[183],"results":[184],"Nvidia":[186],"Tesla":[187],"K80":[188],"GPU.":[189],"envisage":[191],"that":[192],"could":[196],"be":[197],"extended":[198],"improved":[200],"real-time":[202],"thereby":[207],"aiding":[208],"disaster":[209],"response":[210],"personnel":[211],"making":[213],"informed":[214],"decisions.":[215]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
