{"id":"https://openalex.org/W4402265294","doi":"https://doi.org/10.1109/igarss53475.2024.10641440","title":"Urban Land Cover Classification Using Deep Neural Networks Based on VHR Multi-Spectral Image and Point Cloud Integration","display_name":"Urban Land Cover Classification Using Deep Neural Networks Based on VHR Multi-Spectral Image and Point Cloud Integration","publication_year":2024,"publication_date":"2024-07-07","ids":{"openalex":"https://openalex.org/W4402265294","doi":"https://doi.org/10.1109/igarss53475.2024.10641440"},"language":"en","primary_location":{"id":"doi:10.1109/igarss53475.2024.10641440","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10641440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 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/A5085912495","display_name":"Mohamed Fawzy","orcid":"https://orcid.org/0000-0002-4499-0400"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Mohamed Fawzy","raw_affiliation_strings":["Budapest University of Technology and Economics,Faculty of Civil Engineering,Department of Photogrammetry and Geoinformatics,Budapest,Hungary,H-1111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Faculty of Civil Engineering,Department of Photogrammetry and Geoinformatics,Budapest,Hungary,H-1111","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020667500","display_name":"Mohammad Dowajy","orcid":"https://orcid.org/0009-0000-2535-9819"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Mohammad Dowajy","raw_affiliation_strings":["Budapest University of Technology and Economics,Faculty of Civil Engineering,Department of Photogrammetry and Geoinformatics,Budapest,Hungary,H-1111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Faculty of Civil Engineering,Department of Photogrammetry and Geoinformatics,Budapest,Hungary,H-1111","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011353941","display_name":"Tam\u00e1s Lovas","orcid":"https://orcid.org/0000-0001-6588-6437"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Tamas Lovas","raw_affiliation_strings":["Budapest University of Technology and Economics,Faculty of Civil Engineering,Department of Photogrammetry and Geoinformatics,Budapest,Hungary,H-1111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Faculty of Civil Engineering,Department of Photogrammetry and Geoinformatics,Budapest,Hungary,H-1111","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001604522","display_name":"\u00c1rp\u00e1d Barsi","orcid":"https://orcid.org/0000-0002-0298-7502"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Arpad Barsi","raw_affiliation_strings":["Budapest University of Technology and Economics,Faculty of Civil Engineering,Department of Photogrammetry and Geoinformatics,Budapest,Hungary,H-1111"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Faculty of Civil Engineering,Department of Photogrammetry and Geoinformatics,Budapest,Hungary,H-1111","institution_ids":["https://openalex.org/I29770179"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17637893,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"52","issue":null,"first_page":"5377","last_page":"5381"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9958999752998352,"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"}},"topics":[{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9958999752998352,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9919000267982483,"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":0.989300012588501,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.684249758720398},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6502432227134705},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.6178085803985596},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5357344150543213},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5276340842247009},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49235570430755615},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.489124596118927},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4881656765937805},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.48519518971443176},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.46615657210350037},{"id":"https://openalex.org/keywords/cloud-cover","display_name":"Cloud cover","score":0.44361501932144165},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.42301779985427856},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39688557386398315},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33436334133148193},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.28290706872940063},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.26003238558769226},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10237056016921997},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08626917004585266}],"concepts":[{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.684249758720398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6502432227134705},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.6178085803985596},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5357344150543213},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5276340842247009},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49235570430755615},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.489124596118927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4881656765937805},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.48519518971443176},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.46615657210350037},{"id":"https://openalex.org/C206887242","wikidata":"https://www.wikidata.org/wiki/Q830457","display_name":"Cloud cover","level":3,"score":0.44361501932144165},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.42301779985427856},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39688557386398315},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33436334133148193},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.28290706872940063},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.26003238558769226},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10237056016921997},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08626917004585266},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss53475.2024.10641440","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10641440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1984792953","https://openalex.org/W2249153497","https://openalex.org/W2467615243","https://openalex.org/W2504969653","https://openalex.org/W2599857737","https://openalex.org/W2886645875","https://openalex.org/W3009072206","https://openalex.org/W3025050498","https://openalex.org/W3047317383","https://openalex.org/W3088288736","https://openalex.org/W3139026182","https://openalex.org/W3183898570","https://openalex.org/W4246193833","https://openalex.org/W4287093414","https://openalex.org/W4306253158","https://openalex.org/W4312317517","https://openalex.org/W4389331271","https://openalex.org/W6724868818","https://openalex.org/W6884987196"],"related_works":["https://openalex.org/W4244478748","https://openalex.org/W4223488648","https://openalex.org/W2134969820","https://openalex.org/W2251605416","https://openalex.org/W2068630047","https://openalex.org/W2560439919","https://openalex.org/W4389340727","https://openalex.org/W3150465815","https://openalex.org/W2043913960","https://openalex.org/W3129683637"],"abstract_inverted_index":{"The":[0,34,106,163,186],"recent":[1],"development":[2],"in":[3,30,103,115],"computer":[4],"processing":[5],"and":[6,9,20,53,80,113,123,160,182,200,213],"memory":[7],"performance,":[8],"the":[10,128,169,173,184,197,203,207],"growing":[11],"availability":[12],"of":[13,41,135,194,211,216],"satellite":[14,72,140],"imageries":[15],"have":[16],"made":[17],"deep":[18],"learning":[19],"convolutional":[21],"neural":[22,60],"networks":[23],"more":[24],"effective":[25],"with":[26,120,145],"wide":[27],"range":[28],"applications":[29],"urban":[31,35,104],"research":[32],"fields.":[33],"environment":[36],"comprises":[37],"several":[38],"significant":[39],"uses":[40],"remote":[42],"sensing,":[43],"such":[44],"as":[45],"feature":[46],"extraction,":[47],"building":[48],"identification,":[49],"transportation":[50],"system":[51],"management,":[52],"infrastructure":[54],"monitoring.":[55],"AI-based":[56],"algorithms,":[57],"especially":[58],"convolution":[59],"networks,":[61],"are":[62],"characterized":[63],"by":[64,75],"their":[65],"ability":[66],"to":[67,172,180],"achieve":[68],"higher":[69],"performance":[70],"for":[71,99,132,196,202],"image":[73,141],"classification":[74,102,129,170,198,209],"analyzing":[76],"multiple":[77],"accessible":[78],"data":[79,148],"utilizing":[81],"all":[82],"available":[83],"features.":[84],"In":[85],"this":[86],"paper,":[87],"a":[88,116],"Convolutional":[89],"Neural":[90],"Network":[91],"(CNN)":[92],"model,":[93,154],"based":[94],"on":[95],"GoogLeNet,":[96],"was":[97,109],"applied":[98],"land":[100,136],"cover":[101,137],"environment.":[105],"suggested":[107],"model":[108,164,189],"designed,":[110],"trained,":[111],"validated,":[112],"implemented":[114],"built-up":[117],"study":[118],"area":[119],"building,":[121],"road,":[122],"vegetation":[124],"classes.":[125],"To":[126],"enhance":[127],"capabilities":[130],"required":[131],"accurate":[133],"discrimination":[134],"classes,":[138],"WorldView-2":[139],"has":[142],"been":[143],"integrated":[144],"point":[146],"cloud":[147],"which":[149],"enable":[150],"deriving":[151],"digital":[152],"surface":[153,158],"intensity,":[155],"normal":[156],"vector,":[157],"variation,":[159],"vertical":[161],"layers.":[162],"assessment":[165],"process":[166,199],"involved":[167],"comparing":[168],"results":[171,210],"Object":[174],"Based":[175],"Image":[176],"Analysis":[177],"(OBIA)":[178],"method":[179],"verify":[181],"validate":[183],"approach.":[185],"proposed":[187],"CNN":[188],"achieved":[190],"an":[191],"overall":[192],"accuracy":[193,215],"83.25%":[195],"87.00%":[201],"post-classification":[204],"refinement":[205,214],"outperforming":[206],"OBIA":[208],"80.75%":[212],"83.50%.":[217]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}