{"id":"https://openalex.org/W2057785031","doi":"https://doi.org/10.1080/10824009709480493","title":"Road Network Extraction from High Resolution Airborne Digital Camera Data","display_name":"Road Network Extraction from High Resolution Airborne Digital Camera Data","publication_year":1997,"publication_date":"1997-12-01","ids":{"openalex":"https://openalex.org/W2057785031","doi":"https://doi.org/10.1080/10824009709480493","mag":"2057785031"},"language":"en","primary_location":{"id":"doi:10.1080/10824009709480493","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10824009709480493","pdf_url":null,"source":{"id":"https://openalex.org/S4210199948","display_name":"Annals of GIS","issn_l":"1082-4006","issn":["1082-4006","1947-5683","1947-5691","2374-9318"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annals of GIS","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5059264917","display_name":"Peng Gong","orcid":"https://orcid.org/0000-0003-1513-3765"},"institutions":[{"id":"https://openalex.org/I4210122545","display_name":"Center for Assessment","ror":"https://ror.org/02pdqrx22","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210122545"]},{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Gong","raw_affiliation_strings":["Center for Assessment and Monitoring of Forest and Environmental Resources, Department of Environ mental Science, Policy and Management, University of California, Berkeley, CA, 94720-3310, USA","Center for Assessment and Monitoring of Forest and Environmental Resources, Department of Environ mental Science, Policy and Management , University of California , Berkeley , CA , 94720-3310 , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Assessment and Monitoring of Forest and Environmental Resources, Department of Environ mental Science, Policy and Management, University of California, Berkeley, CA, 94720-3310, USA","institution_ids":["https://openalex.org/I4210122545"]},{"raw_affiliation_string":"Center for Assessment and Monitoring of Forest and Environmental Resources, Department of Environ mental Science, Policy and Management , University of California , Berkeley , CA , 94720-3310 , USA","institution_ids":["https://openalex.org/I4210122545","https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006022882","display_name":"Jinfei Wang","orcid":"https://orcid.org/0000-0002-8404-0530"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jinfei Wang","raw_affiliation_strings":["Department of Geography, University of Western Ontario, London, Ontario, Canada, N6A 5B7","Department of Geography , University of Western Ontario , London , Ontario , Canada , N6A 5B7"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography, University of Western Ontario, London, Ontario, Canada, N6A 5B7","institution_ids":["https://openalex.org/I125749732"]},{"raw_affiliation_string":"Department of Geography , University of Western Ontario , London , Ontario , Canada , N6A 5B7","institution_ids":["https://openalex.org/I125749732"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1500,"currency":"USD","value_usd":1500},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.2226289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"3","issue":"1-2","first_page":"51","last_page":"59"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9998999834060669,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.989300012588501,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9815999865531921,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6813790202140808},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6674885749816895},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6618086695671082},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6272357106208801},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5889039039611816},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.48487553000450134},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47898077964782715},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4621458947658539},{"id":"https://openalex.org/keywords/digital-camera","display_name":"Digital camera","score":0.41219115257263184}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6813790202140808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6674885749816895},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6618086695671082},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6272357106208801},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5889039039611816},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.48487553000450134},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47898077964782715},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4621458947658539},{"id":"https://openalex.org/C2779705975","wikidata":"https://www.wikidata.org/wiki/Q62927","display_name":"Digital camera","level":2,"score":0.41219115257263184}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10824009709480493","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10824009709480493","pdf_url":null,"source":{"id":"https://openalex.org/S4210199948","display_name":"Annals of GIS","issn_l":"1082-4006","issn":["1082-4006","1947-5683","1947-5691","2374-9318"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annals of GIS","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W4563702","https://openalex.org/W137200999","https://openalex.org/W181107242","https://openalex.org/W2024570941","https://openalex.org/W2024918151","https://openalex.org/W2033962082","https://openalex.org/W2069001670","https://openalex.org/W2082853639","https://openalex.org/W2088237181","https://openalex.org/W2105536892","https://openalex.org/W2123385460","https://openalex.org/W2315887605","https://openalex.org/W2673969421","https://openalex.org/W2800394774","https://openalex.org/W2913706141","https://openalex.org/W3017143921","https://openalex.org/W4230704136","https://openalex.org/W4247962183","https://openalex.org/W4249617845","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1629127207","https://openalex.org/W4289655544","https://openalex.org/W2044092692","https://openalex.org/W2547665164","https://openalex.org/W3103111272","https://openalex.org/W2284814053","https://openalex.org/W2034196146"],"abstract_inverted_index":{"Most":[0],"road":[1,36,96,143,182,190,208,217,250],"network":[2,97,144],"extraction":[3,98,120,130,153,251],"algorithms":[4,84,124],"developed":[5],"are":[6],"based":[7,57,67],"on":[8,58],"linear":[9,54,74,119,129,152],"analysis":[10,55],"methods.":[11,68],"These":[12],"methods":[13,56],"involve":[14],"search":[15],"of":[16,138,187],"edges":[17],"through":[18,174],"edge":[19],"filtering,":[20,22],"morphological":[21],"or":[23],"gradient":[24,76],"modelling.":[25],"As":[26],"image":[27,122,159,172,194,200],"resolution":[28],"increases":[29],"from":[30,193],"10\u201330":[31],"m":[32,111,240,243],"to":[33,40,155,253],"0.5\u20132":[34],"m,":[35],"networks":[37,218],"will":[38],"appear":[39],"be":[41],"narrow":[42],"areas":[43,222],"rather":[44],"than":[45],"thin":[46],"lines.":[47],"This":[48],"becomes":[49],"a":[50,81,86,92,156],"challenge":[51],"for":[52,65,95,185,249],"traditional":[53],"mask":[59],"operations":[60],"but":[61,223],"creates":[62],"an":[63,72],"opportunity":[64],"classification":[66,83,212],"We":[69],"experimented":[70],"with":[71,108,196,233],"advanced":[73],"analysis,":[75,79],"direction":[77],"profile":[78],"and":[80,91,121,169,241,255],"few":[82],"including":[85],"maximum":[87],"likelihood":[88],"classification,":[89],"clustering":[90,123,201],"contextual":[93,211],"classifier":[94],"using":[99],"airborne":[100],"digital":[101],"camera":[102],"data":[103],"acquired":[104],"over":[105],"Livermore,":[106],"California":[107],"approximately":[109,238],"1.6":[110],"spatial":[112],"resolution.":[113],"Results":[114],"indicate":[115],"that":[116,160],"both":[117],"the":[118,139,151,165,179,188,234,245],"worked":[125],"reasonably":[126],"well.":[127],"The":[128,199,210],"method":[131,202],"requires":[132],"some":[133,225],"preprocessing":[134],"such":[135],"as":[136],"filtering":[137],"original":[140],"image.":[141],"Best":[142],"results":[145],"have":[146],"been":[147],"obtained":[148,192],"by":[149,163],"applying":[150],"algorithm":[154],"morphologically":[157],"filtered":[158],"was":[161],"generated":[162],"combining":[164],"near":[166],"infrared":[167],"(NIR)":[168],"red":[170],"(R)":[171],"bands":[173],"NIR/R+NIR.":[175],"With":[176],"this":[177],"method,":[178],"correctly":[180,206],"extracted":[181,207],"pixels":[183,191],"account":[184],"78.7%":[186],"total":[189],"interpretation":[195],"field":[197],"verification.":[198],"resulted":[203,213],"in":[204,214,219],"74.5%":[205],"pixels.":[209],"relatively":[215],"noise-free":[216],"new":[220],"residential":[221,229],"omitted":[224],"roads":[226],"at":[227,237],"older":[228],"areas.":[230],"When":[231],"experimenting":[232],"images":[235],"resampled":[236],"3":[239],"5":[242],"resolution,":[244],"best":[246],"overall":[247],"accuracies":[248],"decreased":[252],"74.6%":[254],"61.6%,":[256],"respectively.":[257]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}