{"id":"https://openalex.org/W2546821789","doi":"https://doi.org/10.1109/igarss.2016.7729408","title":"Road network extraction via deep learning and line integral convolution","display_name":"Road network extraction via deep learning and line integral convolution","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2546821789","doi":"https://doi.org/10.1109/igarss.2016.7729408","mag":"2546821789"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2016.7729408","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7729408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","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/A5031573889","display_name":"Peikang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peikang Li","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China"],"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002805753","display_name":"Yu Zang","orcid":"https://orcid.org/0000-0002-7193-1414"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zang","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China"],"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100416961","display_name":"Cheng Wang","orcid":"https://orcid.org/0000-0001-6075-796X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Wang","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China"],"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100613889","display_name":"Jonathan Li","orcid":"https://orcid.org/0000-0001-7899-0049"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]},{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CA","CN"],"is_corresponding":false,"raw_author_name":"Jonathan Li","raw_affiliation_strings":["Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada","Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767934","display_name":"Ming Cheng","orcid":"https://orcid.org/0000-0001-6480-6482"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Cheng","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China"],"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104304158","display_name":"Lun Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lun Luo","raw_affiliation_strings":["China Transport Telecommunications&Information Center, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Transport Telecommunications&Information Center, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114506788","display_name":"Yao Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Yu","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China"],"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5031573889"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":7.1943,"has_fulltext":false,"cited_by_count":59,"citation_normalized_percentile":{"value":0.97014746,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1599","last_page":"1602"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":1.0,"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":1.0,"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":0.9968000054359436,"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":0.9645000100135803,"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/convolution","display_name":"Convolution (computer science)","score":0.7334232926368713},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7297083139419556},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7132330536842346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6696395874023438},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6511384844779968},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.564546525478363},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.562188446521759},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5407268404960632},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5242845416069031},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4616142511367798},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4400074779987335},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40280431509017944},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16371792554855347},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12321171164512634}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7334232926368713},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7297083139419556},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7132330536842346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6696395874023438},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6511384844779968},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.564546525478363},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.562188446521759},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5407268404960632},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5242845416069031},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4616142511367798},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4400074779987335},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40280431509017944},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16371792554855347},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12321171164512634},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/igarss.2016.7729408","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7729408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W73112891","https://openalex.org/W1974097572","https://openalex.org/W1984288883","https://openalex.org/W2015785930","https://openalex.org/W2060095247","https://openalex.org/W2073470731","https://openalex.org/W2097375363","https://openalex.org/W2128906900","https://openalex.org/W2147800946","https://openalex.org/W2154956340","https://openalex.org/W2167069501","https://openalex.org/W2172011810","https://openalex.org/W2345157853"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W2964954556","https://openalex.org/W3029198973","https://openalex.org/W3019910406"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,38,57],"learning-based":[6],"road":[7,42,83,107,121],"network":[8,19],"extraction":[9,108,122],"scheme":[10],"from":[11],"high":[12,114],"resolution":[13],"satellite.":[14],"First,":[15],"the":[16,35,67,92,98,105],"convolutional":[17],"neural":[18],"(CNN),":[20],"which":[21],"is":[22,54,63,110],"able":[23,86,111],"to":[24,33,41,47,50,65,70,87,91,112,118],"capture":[25],"large":[26],"context":[27],"of":[28,37,95],"local":[29],"structures,":[30],"are":[31,85],"applied":[32],"predict":[34],"probability":[36],"pixel":[39,49],"belonging":[40],"regions,":[43],"and":[44,97],"assign":[45],"labels":[46],"each":[48],"describe":[51],"whether":[52],"it":[53],"road.":[55],"Then,":[56],"line":[58,99],"integral":[59,100],"convolution":[60,101],"based":[61,102],"algorithm":[62],"developed":[64],"smooth":[66],"rough":[68],"map":[69],"connect":[71],"small":[72],"gaps.":[73],"Finally,":[74],"by":[75],"combining":[76],"with":[77],"some":[78],"common":[79],"image":[80],"processing":[81],"operators,":[82],"centerlines":[84],"be":[88],"acquired.":[89],"Attribute":[90],"learning":[93],"capacity":[94],"CNN,":[96],"connection":[103],"scheme,":[104],"proposed":[106],"method":[109],"provide":[113],"quality":[115],"results":[116],"comparing":[117],"current":[119],"state-of-art":[120],"methods.":[123]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":2}],"updated_date":"2026-04-07T06:01:17.266235","created_date":"2025-10-10T00:00:00"}