{"id":"https://openalex.org/W2166076837","doi":"https://doi.org/10.1109/igarss.2010.5650966","title":"Experimental research on urban road extraction from high-resolution RS images using Probabilistic Topic Models","display_name":"Experimental research on urban road extraction from high-resolution RS images using Probabilistic Topic Models","publication_year":2010,"publication_date":"2010-07-01","ids":{"openalex":"https://openalex.org/W2166076837","doi":"https://doi.org/10.1109/igarss.2010.5650966","mag":"2166076837"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2010.5650966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2010.5650966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5112308087","display_name":"Wenbin Yi","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbin Yi","raw_affiliation_strings":["State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100716311","display_name":"Yunhao Chen","orcid":"https://orcid.org/0000-0001-7926-7303"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhao Chen","raw_affiliation_strings":["State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051316999","display_name":"Hong Tang","orcid":"https://orcid.org/0000-0001-9058-5724"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Tang","raw_affiliation_strings":["ADREM, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ADREM, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037042702","display_name":"Lei Deng","orcid":"https://orcid.org/0000-0002-4574-7381"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Deng","raw_affiliation_strings":["College of Resource, Environment and Tourism, Capital Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Resource, Environment and Tourism, Capital Normal University, Beijing, China","institution_ids":["https://openalex.org/I96852419"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"445","last_page":"448"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9995999932289124,"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":0.9995999932289124,"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/T11106","display_name":"Data Management and Algorithms","score":0.9733999967575073,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10757","display_name":"Geographic Information Systems Studies","score":0.963699996471405,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7410773634910583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7097572088241577},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6791884303092957},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5712383389472961},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5545757412910461},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5531177520751953},{"id":"https://openalex.org/keywords/hough-transform","display_name":"Hough transform","score":0.5032774806022644},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4964023232460022},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4926915764808655},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4170973300933838}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7410773634910583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7097572088241577},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6791884303092957},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5712383389472961},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5545757412910461},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5531177520751953},{"id":"https://openalex.org/C200518788","wikidata":"https://www.wikidata.org/wiki/Q195076","display_name":"Hough transform","level":3,"score":0.5032774806022644},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4964023232460022},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4926915764808655},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4170973300933838}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2010.5650966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2010.5650966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1880262756","https://openalex.org/W1998604612","https://openalex.org/W2053077052","https://openalex.org/W2078219094","https://openalex.org/W2107743791","https://openalex.org/W2121915926","https://openalex.org/W2129004009","https://openalex.org/W2133014420","https://openalex.org/W2149035855","https://openalex.org/W2163994077","https://openalex.org/W2169942580","https://openalex.org/W2356912590","https://openalex.org/W4237791300"],"related_works":["https://openalex.org/W2030098947","https://openalex.org/W1974777989","https://openalex.org/W2003466055","https://openalex.org/W2363834444","https://openalex.org/W2070077862","https://openalex.org/W2044092692","https://openalex.org/W2614621130","https://openalex.org/W4289655544","https://openalex.org/W2547665164","https://openalex.org/W3103111272"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2,27,93],"semi-automated":[3],"algorithm":[4,180],"to":[5,56,63,81,92,114],"extract":[6],"urban":[7],"road":[8,120,132,143,150,171,184],"from":[9,26],"high-resolution":[10,28],"RS":[11],"image":[12,22,29,38,103,158],"using":[13,77],"the":[14,58,78,100,119,139,149,178],"Probabilistic":[15],"Topic":[16],"Models.":[17],"First":[18],"of":[19,60,131,142,148],"all,":[20],"an":[21,182],"collection":[23,39],"is":[24,40,90],"generated":[25],"by":[30,177],"partitioning":[31],"it":[32,111],"into":[33,42],"densely":[34,71],"overlapped":[35,72],"sub-images.":[36,116],"The":[37,51,67,134,145],"divided":[41],"two":[43],"subsets,":[44],"i.e.,":[45],"training":[46,52,68],"images":[47,53,69,168],"and":[48,62,73,161,181],"testing":[49],"images.":[50],"are":[54,70,74],"used":[55],"estimate":[57],"number":[59],"topics,":[61],"learn":[64],"topic":[65,94,108],"models.":[66],"folded":[75],"in":[76,87,99],"learned":[79],"topics":[80],"make":[82],"sure":[83],"that":[84,170],"every":[85,97],"pixel":[86,98],"each":[88],"document":[89],"allocated":[91,106],"label.":[95],"Therefore,":[96],"initial":[101,183],"large":[102],"might":[104,112],"be":[105,127,153,174,187],"multiple":[107,115],"labels":[109,130],"since":[110],"belong":[113],"By":[117],"selecting":[118],"segments":[121,151,172],"samples,":[122],"several":[123],"cluster":[124],"centers":[125],"will":[126,152],"assumed":[128],"as":[129],"objects.":[133],"semantic":[135],"information":[136],"can":[137,173,186],"improve":[138],"extraction":[140],"accuracy":[141],"segments.":[144],"central":[146],"lines":[147],"extracted":[154],"basing":[155],"on":[156],"some":[157],"filter":[159],"algorithms":[160],"Hough":[162],"transform.":[163],"Experimental":[164],"results":[165],"over":[166],"EROS-B":[167],"show":[169],"effectively":[175],"detected":[176],"proposed":[179],"network":[185],"formed.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
