{"id":"https://openalex.org/W7161021205","doi":"https://doi.org/10.48550/arxiv.2605.11863","title":"GATA2Floor: Graph attention for floor counting in street-view facades","display_name":"GATA2Floor: Graph attention for floor counting in street-view facades","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161021205","doi":"https://doi.org/10.48550/arxiv.2605.11863"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11863","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.11863","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136058786","display_name":"Ngoc Anh Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Ngoc Tan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114945797","display_name":"Tzoulio Chamiti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chamiti, Tzoulio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063111181","display_name":"Eirini Papagiannopoulou","orcid":"https://orcid.org/0000-0002-1014-3481"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Papagiannopoulou, Eirini","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043511500","display_name":"Nikos Deligiannis","orcid":"https://orcid.org/0000-0001-9300-5860"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deligiannis, Nikos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5353000164031982,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5353000164031982,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.11219999939203262,"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"}},{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.03460000082850456,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/facade","display_name":"Facade","score":0.8884000182151794},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7006999850273132},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6434999704360962},{"id":"https://openalex.org/keywords/building-automation","display_name":"Building automation","score":0.3407000005245209},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.3312999904155731},{"id":"https://openalex.org/keywords/3d-city-models","display_name":"3D city models","score":0.3046000003814697}],"concepts":[{"id":"https://openalex.org/C2780113678","wikidata":"https://www.wikidata.org/wiki/Q183061","display_name":"Facade","level":2,"score":0.8884000182151794},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7006999850273132},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6434999704360962},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6230999827384949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.508899986743927},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3458000123500824},{"id":"https://openalex.org/C83931994","wikidata":"https://www.wikidata.org/wiki/Q1149653","display_name":"Building automation","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C2778597888","wikidata":"https://www.wikidata.org/wiki/Q172169","display_name":"3D city models","level":3,"score":0.3046000003814697},{"id":"https://openalex.org/C2778530916","wikidata":"https://www.wikidata.org/wiki/Q1352349","display_name":"Building model","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.2750999927520752},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11863","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.11863","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8410239219665527,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automated":[0],"analysis":[1],"of":[2,69,96,130],"building":[3,71],"facades":[4],"from":[5],"street-level":[6],"imagery":[7],"has":[8],"great":[9],"potential":[10],"for":[11,134],"urban":[12],"analytics,":[13],"energy":[14],"assessment,":[15],"and":[16,87,122],"emergency":[17],"planning.":[18],"However,":[19],"it":[20],"requires":[21],"reasoning":[22,105,133],"over":[23,42],"spatially":[24],"arranged":[25],"elements":[26,79],"rather":[27],"than":[28],"solely":[29],"isolated":[30],"detections.":[31],"In":[32],"this":[33],"work,":[34],"we":[35,52,99],"model":[36,62],"each":[37],"facade":[38,135],"as":[39],"a":[40,46,55,70,113],"graph":[41],"window/door":[43],"detections":[44],"with":[45],"vertical":[47],"prior":[48],"on":[49,119],"edges.":[50],"Additionally,":[51],"introduce":[53],"GATA2Floor,":[54],"multi-head":[56],"Graph":[57],"Attention":[58],"v2":[59],"(GATv2)":[60],"based":[61,118],"that":[63,101],"predicts":[64],"the":[65,94,102,128],"global":[66],"floor":[67,82],"count":[68],"and,":[72],"via":[73],"learnable":[74],"cross-attention":[75],"queries,":[76],"softly":[77],"assigns":[78],"to":[80,89],"latent":[81],"slots,":[83],"yielding":[84],"interpretable":[85],"outputs":[86],"robustness":[88],"irregular":[90],"designs.":[91],"To":[92],"mitigate":[93],"lack":[95],"labeled":[97],"datasets,":[98],"demonstrate":[100],"proposed":[103],"graph-based":[104],"can":[106],"be":[107],"applied":[108],"without":[109],"annotations":[110],"by":[111],"leveraging":[112],"lightweight":[114],"label-free":[115],"proposal":[116],"mechanism":[117],"self-supervised":[120],"features":[121],"vision-language":[123],"scoring.":[124],"Our":[125],"approach":[126],"demonstrates":[127],"value":[129],"graph-attention-based":[131],"relational":[132],"understanding.":[136]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}