{"id":"https://openalex.org/W2891512280","doi":"https://doi.org/10.1109/aipr.2017.8457973","title":"A Benchmark for Building Footprint Classification Using Orthorectified RGB Imagery and Digital Surface Models from Commercial Satellites","display_name":"A Benchmark for Building Footprint Classification Using Orthorectified RGB Imagery and Digital Surface Models from Commercial Satellites","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2891512280","doi":"https://doi.org/10.1109/aipr.2017.8457973","mag":"2891512280"},"language":"en","primary_location":{"id":"doi:10.1109/aipr.2017.8457973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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/A5064249539","display_name":"Hirsh Goldberg","orcid":null},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hirsh Goldberg","raw_affiliation_strings":["JHU Applied Physics Laboratory, Maryland, Laurel, USA"],"affiliations":[{"raw_affiliation_string":"JHU Applied Physics Laboratory, Maryland, Laurel, USA","institution_ids":["https://openalex.org/I2802946424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057680791","display_name":"Myron Brown","orcid":"https://orcid.org/0000-0001-8302-2106"},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Myron Brown","raw_affiliation_strings":["JHU Applied Physics Laboratory, Maryland, Laurel, USA"],"affiliations":[{"raw_affiliation_string":"JHU Applied Physics Laboratory, Maryland, Laurel, USA","institution_ids":["https://openalex.org/I2802946424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031464972","display_name":"Sean Wang","orcid":"https://orcid.org/0000-0002-4567-3263"},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sean Wang","raw_affiliation_strings":["JHU Applied Physics Laboratory, Maryland, Laurel, USA"],"affiliations":[{"raw_affiliation_string":"JHU Applied Physics Laboratory, Maryland, Laurel, USA","institution_ids":["https://openalex.org/I2802946424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5064249539"],"corresponding_institution_ids":["https://openalex.org/I2802946424"],"apc_list":null,"apc_paid":null,"fwci":1.4957,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.81345635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9995999932289124,"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":0.9980999827384949,"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/T12983","display_name":"Satellite Image Processing and Photogrammetry","score":0.9955999851226807,"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/orthophoto","display_name":"Orthophoto","score":0.8572672009468079},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.7159626483917236},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6962992548942566},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6562737226486206},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6451965570449829},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5992199182510376},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.55253005027771},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.5484878420829773},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.536316454410553},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5269275903701782},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.43801411986351013},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4369528293609619},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.4222607910633087},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40392157435417175},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.1286470890045166},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10255685448646545},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10249704122543335}],"concepts":[{"id":"https://openalex.org/C82789328","wikidata":"https://www.wikidata.org/wiki/Q922585","display_name":"Orthophoto","level":2,"score":0.8572672009468079},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.7159626483917236},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6962992548942566},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6562737226486206},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6451965570449829},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5992199182510376},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.55253005027771},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.5484878420829773},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.536316454410553},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5269275903701782},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.43801411986351013},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4369528293609619},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.4222607910633087},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40392157435417175},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.1286470890045166},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10255685448646545},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10249704122543335},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr.2017.8457973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8100000023841858}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"},{"id":"https://openalex.org/F4320334118","display_name":"United States Special Operations Command","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1824831302","https://openalex.org/W2018839022","https://openalex.org/W2045143156","https://openalex.org/W2114828048","https://openalex.org/W2134337515","https://openalex.org/W2155653432","https://openalex.org/W2374387732","https://openalex.org/W2415553974","https://openalex.org/W2559545830","https://openalex.org/W2601953997","https://openalex.org/W2609402060","https://openalex.org/W2620972937","https://openalex.org/W2621036493","https://openalex.org/W2623331213","https://openalex.org/W2740102162","https://openalex.org/W2746824153","https://openalex.org/W2971095420","https://openalex.org/W4243400037","https://openalex.org/W4302799888","https://openalex.org/W6638713260","https://openalex.org/W6683335404","https://openalex.org/W6708959052","https://openalex.org/W6737021809","https://openalex.org/W6977579133"],"related_works":["https://openalex.org/W1532075541","https://openalex.org/W2368026034","https://openalex.org/W2367777051","https://openalex.org/W3087014565","https://openalex.org/W1657914084","https://openalex.org/W1986643118","https://openalex.org/W292456094","https://openalex.org/W4284714707","https://openalex.org/W4368357547","https://openalex.org/W4224312031"],"abstract_inverted_index":{"Identifying":[0],"building":[1,37,102,206],"footprints":[2,38],"is":[3,135,153],"a":[4,22,88,171,177],"critical":[5],"and":[6,60,68,93,107,119,141,208,223],"challenging":[7],"problem":[8,17,78],"in":[9,229],"many":[10],"remote":[11],"sensing":[12,25,81],"applications.":[13],"Solutions":[14],"to":[15,131,169,225,238],"this":[16,30,77,165,230,234],"have":[18],"been":[19],"investigated":[20],"using":[21,79,105],"variety":[23],"of":[24,36,90,126,144,167,182],"modalities":[26,82],"as":[27,111,129,138,191,193],"input.":[28],"In":[29,217],"work,":[31,95],"we":[32,209,219],"consider":[33],"the":[34,69,139,145,150,239],"detection":[35,104],"from":[39,46,187],"3D":[40,108,117,146],"Digital":[41],"Surface":[42],"Models":[43],"(DSMs)":[44],"created":[45,186],"commercial":[47,188],"satellite":[48,189],"imagery":[49,128,152,199],"along":[50],"with":[51,164],"RGB":[52,197],"orthorectified":[53,198],"imagery.":[54],"Recent":[55],"public":[56,178,235],"challenges":[57,92],"(SpaceNet":[58],"1":[59],"2,":[61],"DSTL":[62],"Satellite":[63],"Imagery":[64],"Feature":[65],"Detection":[66],"Challenge,":[67],"ISPRS":[70],"Test":[71],"Project":[72],"on":[73],"Urban":[74],"Classification)":[75],"approach":[76],"other":[80,94],"or":[83,123,149],"higher":[84],"resolution":[85],"data.":[86],"As":[87],"result":[89],"these":[91],"most":[96,156],"publically":[97],"available":[98],"automated":[99,173],"methods":[100],"for":[101,115,214],"footprint":[103],"2D":[106,120,151],"data":[109,148,168],"sources":[110],"input":[112],"are":[113],"meant":[114],"high-resolution":[116],"lidar":[118,147],"airborne":[121],"imagery,":[122,190],"make":[124],"use":[125],"multispectral":[127],"well":[130,162,192],"aid":[132],"detection.":[133],"Performance":[134],"typically":[136],"degraded":[137],"fidelity":[140],"post":[142],"spacing":[143],"reduced.":[154],"Furthermore,":[155],"software":[157],"packages":[158],"do":[159],"not":[160],"work":[161],"enough":[163],"type":[166],"enable":[170],"fully":[172],"solution.":[174],"We":[175],"describe":[176],"benchmark":[179,236],"dataset":[180,202,237],"consisting":[181],"50":[183,195],"cm":[184,196],"DSMs":[185],"coincident":[194],"products.":[200],"The":[201],"includes":[203],"ground":[204],"truth":[205],"outlines":[207],"propose":[210],"representative":[211],"quantitative":[212],"metrics":[213],"evaluating":[215],"performance.":[216],"addition,":[218],"provide":[220],"lessons":[221],"learned":[222],"hope":[224],"promote":[226],"additional":[227],"research":[228],"field":[231],"by":[232],"releasing":[233],"community.":[240]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
