{"id":"https://openalex.org/W3013274927","doi":"https://doi.org/10.3390/s20071832","title":"Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network","display_name":"Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network","publication_year":2020,"publication_date":"2020-03-26","ids":{"openalex":"https://openalex.org/W3013274927","doi":"https://doi.org/10.3390/s20071832","mag":"3013274927","pmid":"https://pubmed.ncbi.nlm.nih.gov/32224939"},"language":"en","primary_location":{"id":"doi:10.3390/s20071832","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20071832","pdf_url":"https://www.mdpi.com/1424-8220/20/7/1832/pdf?version=1586089760","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/20/7/1832/pdf?version=1586089760","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033906757","display_name":"Shenggu Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shenggu Yuan","raw_affiliation_strings":["China Transport Telecommunications and Information Center, Beijing 100011, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Transport Telecommunications and Information Center, Beijing 100011, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037028903","display_name":"Ke Yang","orcid":"https://orcid.org/0000-0001-6617-9558"},"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"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Ke Yang","raw_affiliation_strings":["Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353783","display_name":"Xin Li","orcid":"https://orcid.org/0000-0002-0144-9489"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin Li","raw_affiliation_strings":["Guojiao Spatial Information Technology (Beijing) Co., Ltd., Beijing 100011, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guojiao Spatial Information Technology (Beijing) Co., Ltd., Beijing 100011, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100842630","display_name":"Hongyue Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongyue Cai","raw_affiliation_strings":["Guojiao Spatial Information Technology (Beijing) Co., Ltd., Beijing 100011, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guojiao Spatial Information Technology (Beijing) Co., Ltd., Beijing 100011, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5037028903"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":1.9563,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.84463882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"20","issue":"7","first_page":"1832","last_page":"1832"},"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":0.9983999729156494,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/orthophoto","display_name":"Orthophoto","score":0.9230445623397827},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6313537359237671},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6179412007331848},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5799448490142822},{"id":"https://openalex.org/keywords/dijkstras-algorithm","display_name":"Dijkstra's algorithm","score":0.5364364385604858},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5174092054367065},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5063308477401733},{"id":"https://openalex.org/keywords/binary-image","display_name":"Binary image","score":0.4493924081325531},{"id":"https://openalex.org/keywords/shortest-path-problem","display_name":"Shortest path problem","score":0.4158073663711548},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4126982092857361},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3287920355796814},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.1258162260055542}],"concepts":[{"id":"https://openalex.org/C82789328","wikidata":"https://www.wikidata.org/wiki/Q922585","display_name":"Orthophoto","level":2,"score":0.9230445623397827},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6313537359237671},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6179412007331848},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5799448490142822},{"id":"https://openalex.org/C173870130","wikidata":"https://www.wikidata.org/wiki/Q8548","display_name":"Dijkstra's algorithm","level":4,"score":0.5364364385604858},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5174092054367065},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5063308477401733},{"id":"https://openalex.org/C193828747","wikidata":"https://www.wikidata.org/wiki/Q864118","display_name":"Binary image","level":4,"score":0.4493924081325531},{"id":"https://openalex.org/C22590252","wikidata":"https://www.wikidata.org/wiki/Q1058754","display_name":"Shortest path problem","level":3,"score":0.4158073663711548},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4126982092857361},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3287920355796814},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.1258162260055542},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/s20071832","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20071832","pdf_url":"https://www.mdpi.com/1424-8220/20/7/1832/pdf?version=1586089760","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:32224939","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32224939","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:082eae06a2da4f2a83f62f414a7d5030","is_oa":true,"landing_page_url":"https://doaj.org/article/082eae06a2da4f2a83f62f414a7d5030","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 20, Iss 7, p 1832 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/20/7/1832/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/s20071832","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7180862","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7180862","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s20071832","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20071832","pdf_url":"https://www.mdpi.com/1424-8220/20/7/1832/pdf?version=1586089760","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G6562015932","display_name":null,"funder_award_id":"41901388","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W3013274927.pdf"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W1970800786","https://openalex.org/W1982254880","https://openalex.org/W1986557861","https://openalex.org/W1998211760","https://openalex.org/W2006395152","https://openalex.org/W2018680401","https://openalex.org/W2021193738","https://openalex.org/W2027302630","https://openalex.org/W2048292156","https://openalex.org/W2064972255","https://openalex.org/W2072176627","https://openalex.org/W2082923148","https://openalex.org/W2097853527","https://openalex.org/W2101584813","https://openalex.org/W2105949881","https://openalex.org/W2108598243","https://openalex.org/W2118080570","https://openalex.org/W2133059825","https://openalex.org/W2144572173","https://openalex.org/W2169528473","https://openalex.org/W2194775991","https://openalex.org/W2215966420","https://openalex.org/W2222612055","https://openalex.org/W2286878445","https://openalex.org/W2369266766","https://openalex.org/W2522754410","https://openalex.org/W2560753879","https://openalex.org/W2586644724","https://openalex.org/W2595964094","https://openalex.org/W2731489139","https://openalex.org/W2735039185","https://openalex.org/W2739916017","https://openalex.org/W2757292468","https://openalex.org/W2766666090","https://openalex.org/W2769205678","https://openalex.org/W2774320778","https://openalex.org/W2790841469","https://openalex.org/W2791319857","https://openalex.org/W2804199516","https://openalex.org/W2888772826","https://openalex.org/W2893801697","https://openalex.org/W2896220006","https://openalex.org/W2907583485","https://openalex.org/W2963591855","https://openalex.org/W3105636206","https://openalex.org/W4285719527","https://openalex.org/W6679119569","https://openalex.org/W6680096826","https://openalex.org/W6686692247"],"related_works":["https://openalex.org/W3209137076","https://openalex.org/W4223969905","https://openalex.org/W2374560440","https://openalex.org/W1490490684","https://openalex.org/W4221129498","https://openalex.org/W2887026015","https://openalex.org/W2361442013","https://openalex.org/W2373384475","https://openalex.org/W4310124294","https://openalex.org/W143403600"],"abstract_inverted_index":{"Image":[0],"mosaicking":[1,43],"which":[2,156],"is":[3,16,33,114],"a":[4,11,75,228],"process":[5],"of":[6,18,25,35,44,52,65,78,139,182,190,219],"constructing":[7],"multiple":[8],"orthoimages":[9],"into":[10],"single":[12],"seamless":[13],"composite":[14],"orthoimage,":[15],"one":[17,34],"the":[19,23,36,41,50,63,87,98,102,110,117,126,135,140,144,152,157,161,176,188,191,199,203,208,215,220,223],"key":[20],"steps":[21],"for":[22,91],"production":[24],"large-scale":[26],"digital":[27],"orthophoto":[28],"maps":[29],"(DOM).":[30],"Seamline":[31],"determination":[32,80],"most":[37],"difficult":[38],"technologies":[39],"in":[40,143,155],"automatic":[42],"orthoimages.":[45],"The":[46,149,179],"seamlines":[47,99,158,163,200],"that":[48],"follow":[49,214],"centerlines":[51],"roads":[53],"where":[54],"no":[55],"significant":[56],"differences":[57],"exist":[58],"are":[59,131,151,164],"beneficial":[60],"to":[61],"improve":[62],"quality":[64],"image":[66,93],"mosaicking.":[67,94],"Based":[68],"on":[69,82],"this":[70,72],"idea,":[71],"paper":[73],"proposes":[74],"novel":[76],"method":[77,96,205,225],"seamline":[79],"based":[81],"road":[83,111,128,136],"probability":[84,112,137],"map":[85,113,138],"from":[86],"D-LinkNet":[88,118],"neural":[89,119],"network":[90,120],"urban":[92],"This":[95],"optimizes":[97],"at":[100,175],"both":[101],"semantic":[103],"and":[104,121,146,212],"pixel":[105,177],"level":[106],"as":[107],"follows.":[108],"First,":[109],"obtained":[115,201],"with":[116,172,195],"related":[122],"post":[123],"processing.":[124],"Second,":[125],"preferred":[127],"areas":[129,154],"(PRAs)":[130],"determined":[132,165],"by":[133,166,202],"binarizing":[134],"overlapping":[141],"area":[142],"left":[145],"right":[147],"image.":[148],"PRAs":[150],"priority":[153],"cross.":[159],"Finally,":[160],"final":[162],"Dijkstra\u2019s":[167],"shortest":[168],"path":[169],"algorithm":[170],"implemented":[171],"binary":[173],"min-heap":[174],"level.":[178],"experimental":[180],"results":[181],"three":[183],"group":[184],"data":[185],"sets":[186],"show":[187],"advantages":[189],"proposed":[192,204,224],"method.":[193],"Compared":[194],"two":[196],"previous":[197],"methods,":[198],"pass":[206],"through":[207],"less":[209],"obvious":[210],"objects":[211],"mainly":[213],"roads.":[216],"In":[217],"terms":[218],"computational":[221],"efficiency,":[222],"also":[226],"has":[227],"high":[229],"efficiency.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2025-10-10T00:00:00"}