{"id":"https://openalex.org/W4386352555","doi":"https://doi.org/10.1109/civemsa57781.2023.10231019","title":"Methods for Predictive Performance Improvement of Deep Learning Systems for Aerial Building Roof Detection with Multispectral Images","display_name":"Methods for Predictive Performance Improvement of Deep Learning Systems for Aerial Building Roof Detection with Multispectral Images","publication_year":2023,"publication_date":"2023-06-12","ids":{"openalex":"https://openalex.org/W4386352555","doi":"https://doi.org/10.1109/civemsa57781.2023.10231019"},"language":"en","primary_location":{"id":"doi:10.1109/civemsa57781.2023.10231019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa57781.2023.10231019","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 Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5027842924","display_name":"A. Churi","orcid":null},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Churi","raw_affiliation_strings":["University of Massachusetts,Center for Human/Machine Intelligence, Networking, and Distributed Systems,Department of Electrical and Computer Engineering,Lowell,MA,USA","Department of Electrical and Computer Engineering, Center for Human/Machine Intelligence, Networking, and Distributed Systems, University of Massachusetts, Lowell, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts,Center for Human/Machine Intelligence, Networking, and Distributed Systems,Department of Electrical and Computer Engineering,Lowell,MA,USA","institution_ids":["https://openalex.org/I133738476"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Center for Human/Machine Intelligence, Networking, and Distributed Systems, University of Massachusetts, Lowell, MA, USA","institution_ids":["https://openalex.org/I133738476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001646142","display_name":"Dalila B. Megherbi","orcid":"https://orcid.org/0000-0002-1282-597X"},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"D. B. Megherbi","raw_affiliation_strings":["University of Massachusetts,Center for Human/Machine Intelligence, Networking, and Distributed Systems,Department of Electrical and Computer Engineering,Lowell,MA,USA","Department of Electrical and Computer Engineering, Center for Human/Machine Intelligence, Networking, and Distributed Systems, University of Massachusetts, Lowell, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts,Center for Human/Machine Intelligence, Networking, and Distributed Systems,Department of Electrical and Computer Engineering,Lowell,MA,USA","institution_ids":["https://openalex.org/I133738476"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Center for Human/Machine Intelligence, Networking, and Distributed Systems, University of Massachusetts, Lowell, MA, USA","institution_ids":["https://openalex.org/I133738476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I133738476"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9969000220298767,"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":0.9969000220298767,"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/T11963","display_name":"Impact of Light on Environment and Health","score":0.9779000282287598,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.8229710459709167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7246642112731934},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.690853476524353},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6903746128082275},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6138644814491272},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5462980270385742},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5293679237365723},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5112016797065735},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5086982846260071},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49337783455848694},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44604623317718506},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1873072385787964}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8229710459709167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7246642112731934},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.690853476524353},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6903746128082275},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6138644814491272},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5462980270385742},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5293679237365723},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5112016797065735},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5086982846260071},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49337783455848694},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44604623317718506},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1873072385787964}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/civemsa57781.2023.10231019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa57781.2023.10231019","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 Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1901129140","https://openalex.org/W2102605133","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2395611524","https://openalex.org/W2887935602","https://openalex.org/W2963037989","https://openalex.org/W2963446712","https://openalex.org/W3106250896","https://openalex.org/W4286696412","https://openalex.org/W4318603606","https://openalex.org/W6620707391","https://openalex.org/W6629368666","https://openalex.org/W6687483927","https://openalex.org/W6725739302"],"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/W2792279927","https://openalex.org/W4403391793","https://openalex.org/W4385497869","https://openalex.org/W283587633"],"abstract_inverted_index":{"In":[0,93],"this":[1,94],"paper,":[2,95],"we":[3,96,107,119,137],"present":[4,97],"a":[5,62,111,158,201],"dilated":[6],"convolutional":[7],"neural":[8],"network-based":[9],"deep":[10],"learning":[11],"system":[12,37,198],"for":[13,89,114,148,161,169],"the":[14,29,53,67,86,98,128,171,175,191,212,216],"challenging":[15],"problem":[16],"of":[17,69,85,190,215],"building":[18,149,184,218],"accurate":[19],"roof":[20,150,185],"pixel":[21,186],"detection":[22,104,219],"from":[23,133],"city":[24,116,163],"aerial":[25],"multispectral":[26,134],"images":[27,88],"in":[28,183,188,204,225],"visible":[30],"and":[31,45,82,109,141],"infrared":[32],"spectral":[33,41],"bands.":[34],"The":[35],"proposed":[36],"uses":[38],"an":[39,46,121,139],"automatic":[40,47,142],"band":[42,123],"selection":[43,124],"strategy":[44,125],"histogram-based":[48,143],"threshold-finding":[49],"method":[50],"to":[51,101,126,208],"improve":[52],"network's":[54],"prediction":[55,91,187],"accuracy":[56],"performance.":[57],"Current":[58],"existing":[59],"systems":[60,75],"train":[61],"single":[63],"model":[64,113,160,168],"on":[65],"all":[66,170],"cities":[68,173,192],"interest":[70],"data":[71],"at":[72],"once.":[73],"These":[74],"also":[76,199],"usually":[77],"use":[78],"only":[79,166],"RGB":[80],"bands":[81,132],"avoid":[83],"post-processing":[84],"predicted":[87],"increased":[90],"accuracies.":[92],"following":[99],"solutions":[100],"these":[102],"object":[103,151],"challenges,":[105],"1)":[106],"propose":[108,120,138],"implement":[110],"separate":[112,159],"each":[115,162],"dataset,":[117],"2)":[118],"optimal":[122,144],"choose":[127],"most":[129],"informative":[130],"visible-infrared":[131],"images,":[135],"3)":[136],"adaptive":[140],"threshold":[145],"finding":[146],"technique":[147],"segmentation.":[152],"Our":[153],"results":[154],"show":[155,195],"that":[156,196],"having":[157],"rather":[164],"than":[165],"one":[167],"combined":[172],"increases":[174],"system's":[176],"performance":[177],"by":[178],"20.77":[179],"<sup":[180],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[181],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">%</sup>":[182],"any":[189],"considered.":[193],"We":[194],"our":[197],"has":[200],"35.6%":[202],"increase":[203],"F1":[205],"score":[206],"compared":[207],"its":[209],"state-of-the-art":[210],"counterpart,":[211],"winning":[213],"implementation":[214],"SpaceNet":[217],"national":[220],"challenge,":[221],"which":[222],"took":[223],"place":[224],"2017.":[226]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}