{"id":"https://openalex.org/W1505602352","doi":"https://doi.org/10.1109/jurse.2015.7120490","title":"Structured prediction for urban scene semantic segmentation with geographic context","display_name":"Structured prediction for urban scene semantic segmentation with geographic context","publication_year":2015,"publication_date":"2015-03-01","ids":{"openalex":"https://openalex.org/W1505602352","doi":"https://doi.org/10.1109/jurse.2015.7120490","mag":"1505602352"},"language":"en","primary_location":{"id":"doi:10.1109/jurse.2015.7120490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jurse.2015.7120490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Joint Urban Remote Sensing Event (JURSE)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.research.ed.ac.uk/en/publications/92aa9c70-7826-4d23-87f0-315f7d77166f","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037775056","display_name":"Michele Volpi","orcid":"https://orcid.org/0000-0003-2771-0750"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Michele Volpi","raw_affiliation_strings":["CALVIN, University of Edinburgh, Scotland (UK)","CALVIN, The University of Edinburgh, Scotland (UK)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CALVIN, University of Edinburgh, Scotland (UK)","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"CALVIN, The University of Edinburgh, Scotland (UK)","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101423778","display_name":"Vittorio Ferrari","orcid":"https://orcid.org/0000-0002-1942-233X"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Vittorio Ferrari","raw_affiliation_strings":["CALVIN, University of Edinburgh, Scotland (UK)","CALVIN, The University of Edinburgh, Scotland (UK)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CALVIN, University of Edinburgh, Scotland (UK)","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"CALVIN, The University of Edinburgh, Scotland (UK)","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9976000189781189,"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"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9952999949455261,"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/computer-science","display_name":"Computer science","score":0.7417211532592773},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.6893429756164551},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.6737573146820068},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6186578273773193},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.616283118724823},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6127211451530457},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5925848484039307},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5639453530311584},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4823250472545624},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4203881323337555},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39593884348869324},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35512274503707886},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.3468305468559265},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.32969197630882263},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.1820945143699646}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7417211532592773},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.6893429756164551},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.6737573146820068},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6186578273773193},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.616283118724823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6127211451530457},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5925848484039307},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5639453530311584},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4823250472545624},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4203881323337555},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39593884348869324},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35512274503707886},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.3468305468559265},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.32969197630882263},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.1820945143699646},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/jurse.2015.7120490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jurse.2015.7120490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Joint Urban Remote Sensing Event (JURSE)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/92aa9c70-7826-4d23-87f0-315f7d77166f","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/92aa9c70-7826-4d23-87f0-315f7d77166f","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Volpi, M & Ferrari, V 2015, Structured prediction for urban scene semantic segmentation with geographic context. in Urban Remote Sensing Event (JURSE), 2015 Joint. Institute of Electrical and Electronics Engineers, pp. 1-4. https://doi.org/10.1109/JURSE.2015.7120490","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.ed.ac.uk:publications/92aa9c70-7826-4d23-87f0-315f7d77166f","is_oa":false,"landing_page_url":"http://hdl.handle.net/20.500.11820/92aa9c70-7826-4d23-87f0-315f7d77166f","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:openaire/92aa9c70-7826-4d23-87f0-315f7d77166f","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/92aa9c70-7826-4d23-87f0-315f7d77166f","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Volpi, M & Ferrari, V 2015, Structured prediction for urban scene semantic segmentation with geographic context. in Urban Remote Sensing Event (JURSE), 2015 Joint. Institute of Electrical and Electronics Engineers, pp. 1-4. https://doi.org/10.1109/JURSE.2015.7120490","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8299999833106995,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1748750709","https://openalex.org/W1974524700","https://openalex.org/W1999478155","https://openalex.org/W2001298023","https://openalex.org/W2017313218","https://openalex.org/W2037772847","https://openalex.org/W2066941820","https://openalex.org/W2081293863","https://openalex.org/W2083597815","https://openalex.org/W2100588357","https://openalex.org/W2131743987","https://openalex.org/W2132424470","https://openalex.org/W2143516773","https://openalex.org/W2153396823","https://openalex.org/W2911964244"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2093471820","https://openalex.org/W50079190","https://openalex.org/W2114846443","https://openalex.org/W3102147106","https://openalex.org/W2487162673","https://openalex.org/W3006655138","https://openalex.org/W2963336968","https://openalex.org/W2805329439","https://openalex.org/W2114428692"],"abstract_inverted_index":{"In":[0],"this":[1,23,46],"work":[2],"we":[3,48],"address":[4],"the":[5,27,61,103,117,123],"problem":[6],"of":[7,10,30,42,60,69,109,139],"semantic":[8],"segmentation":[9],"urban":[11],"remote":[12],"sensing":[13],"images":[14],"into":[15,81],"land":[16,70],"cover":[17,71],"maps.":[18],"We":[19,73,101,127],"propose":[20,74],"to":[21,35,75,113],"tackle":[22],"task":[24],"by":[25,122,131],"learning":[26],"geographic":[28,78],"context":[29,79],"classes":[31],"and":[32,66],"use":[33],"it":[34],"favor":[36],"or":[37],"discourage":[38],"certain":[39],"spatial":[40,54,125],"configuration":[41],"label":[43],"assignments.":[44],"For":[45],"reason,":[47],"learn":[49],"from":[50,95],"training":[51],"data":[52],"two":[53],"priors":[55],"enforcing":[56],"different":[57],"key":[58],"aspects":[59],"geographical":[62],"space:":[63],"local":[64],"co-occurrence":[65],"relative":[67],"location":[68],"classes.":[72],"embed":[76],"these":[77],"potentials":[80,94],"a":[82,96,106,137],"pairwise":[83],"conditional":[84],"random":[85,97],"field":[86],"(CRF)":[87],"which":[88,111],"models":[89],"them":[90],"jointly":[91],"with":[92],"unary":[93],"forest":[98],"(RF)":[99],"classifier.":[100],"train":[102],"RF":[104],"on":[105,136],"large":[107],"set":[108,138],"descriptors":[110],"allow":[112],"properly":[114],"account":[115],"for":[116],"class":[118],"appearance":[119],"variations":[120],"induced":[121],"high":[124],"resolution.":[126],"evaluate":[128],"our":[129],"approach":[130],"an":[132],"exhaustive":[133],"experimental":[134],"comparisons":[135],"20":[140],"QuickBird":[141],"pansharpened":[142],"multi-spectral":[143],"images.":[144]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}