{"id":"https://openalex.org/W4361276602","doi":"https://doi.org/10.3390/rs15071829","title":"LDANet: A Lightweight Dynamic Addition Network for Rural Road Extraction from Remote Sensing Images","display_name":"LDANet: A Lightweight Dynamic Addition Network for Rural Road Extraction from Remote Sensing Images","publication_year":2023,"publication_date":"2023-03-29","ids":{"openalex":"https://openalex.org/W4361276602","doi":"https://doi.org/10.3390/rs15071829"},"language":"en","primary_location":{"id":"doi:10.3390/rs15071829","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15071829","pdf_url":"https://www.mdpi.com/2072-4292/15/7/1829/pdf?version=1680096971","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"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":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/15/7/1829/pdf?version=1680096971","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085401538","display_name":"Bohua Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bohua Liu","raw_affiliation_strings":["College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"],"affiliations":[{"raw_affiliation_string":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103077037","display_name":"Jianli Ding","orcid":"https://orcid.org/0000-0002-9626-7660"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianli Ding","raw_affiliation_strings":["College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"],"affiliations":[{"raw_affiliation_string":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101857280","display_name":"Jie Zou","orcid":"https://orcid.org/0000-0002-2183-7614"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Zou","raw_affiliation_strings":["College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"],"affiliations":[{"raw_affiliation_string":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112863646","display_name":"Jinjie Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinjie Wang","raw_affiliation_strings":["College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"],"affiliations":[{"raw_affiliation_string":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074948475","display_name":"Shuai Huang","orcid":"https://orcid.org/0000-0003-3054-5629"},"institutions":[{"id":"https://openalex.org/I196934937","display_name":"Liaocheng University","ror":"https://ror.org/03yh0n709","country_code":"CN","type":"education","lineage":["https://openalex.org/I196934937"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Huang","raw_affiliation_strings":["College of Geography and Environment, Liaocheng University, Liaocheng 252000, China"],"affiliations":[{"raw_affiliation_string":"College of Geography and Environment, Liaocheng University, Liaocheng 252000, China","institution_ids":["https://openalex.org/I196934937"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103077037"],"corresponding_institution_ids":["https://openalex.org/I96908189"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.9393,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.90235108,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"15","issue":"7","first_page":"1829","last_page":"1829"},"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":0.9969000220298767,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9746000170707703,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/computer-science","display_name":"Computer science","score":0.7972744107246399},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5340601205825806},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4954761266708374},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4578598141670227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44457584619522095},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4339603781700134},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41426581144332886},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38352787494659424},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3480193018913269},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12695619463920593},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12439140677452087}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7972744107246399},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5340601205825806},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4954761266708374},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4578598141670227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44457584619522095},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4339603781700134},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41426581144332886},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38352787494659424},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3480193018913269},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12695619463920593},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12439140677452087}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15071829","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15071829","pdf_url":"https://www.mdpi.com/2072-4292/15/7/1829/pdf?version=1680096971","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"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":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:086ee19dcd7b4f58bac49421d3e1526d","is_oa":true,"landing_page_url":"https://doaj.org/article/086ee19dcd7b4f58bac49421d3e1526d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 7, p 1829 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/7/1829/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15071829","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":"Remote Sensing; Volume 15; Issue 7; Pages: 1829","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15071829","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15071829","pdf_url":"https://www.mdpi.com/2072-4292/15/7/1829/pdf?version=1680096971","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"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":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G4401264798","display_name":null,"funder_award_id":"41961059","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4709502298","display_name":null,"funder_award_id":"42171269","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7018839127","display_name":null,"funder_award_id":"2021D01D06","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/W4361276602.pdf"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2009742404","https://openalex.org/W2097375363","https://openalex.org/W2100495367","https://openalex.org/W2172692166","https://openalex.org/W2345157853","https://openalex.org/W2395811491","https://openalex.org/W2461141889","https://openalex.org/W2466601562","https://openalex.org/W2547880720","https://openalex.org/W2762439315","https://openalex.org/W2804199516","https://openalex.org/W2883780447","https://openalex.org/W2893801697","https://openalex.org/W2896446634","https://openalex.org/W2900518108","https://openalex.org/W2914974669","https://openalex.org/W2962772649","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963418739","https://openalex.org/W2964309882","https://openalex.org/W2967073193","https://openalex.org/W2968614659","https://openalex.org/W2996106355","https://openalex.org/W2996290406","https://openalex.org/W3010250471","https://openalex.org/W3015788359","https://openalex.org/W3086017879","https://openalex.org/W3097090744","https://openalex.org/W3105367394","https://openalex.org/W3129042754","https://openalex.org/W3134988213","https://openalex.org/W3158580822","https://openalex.org/W3164151969","https://openalex.org/W4292656663","https://openalex.org/W4313650749","https://openalex.org/W4382897028","https://openalex.org/W6711850960","https://openalex.org/W6762585180","https://openalex.org/W6790679990","https://openalex.org/W6794938427","https://openalex.org/W6841924617"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4304166257","https://openalex.org/W4294635752","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W4379231730","https://openalex.org/W2890372105"],"abstract_inverted_index":{"Automatic":[0],"road":[1,12,47,68,182],"extraction":[2,32,48,99,183],"from":[3,220],"remote":[4,158,221],"sensing":[5,159,222],"images":[6],"has":[7,211],"an":[8,83,123],"important":[9],"impact":[10],"on":[11,165,204],"maintenance":[13],"and":[14,36,42,75,122,148,190,194,202,216],"land":[15],"management.":[16],"While":[17],"significant":[18],"deep-learning-based":[19],"approaches":[20],"have":[21],"been":[22],"developed":[23,58],"in":[24,96,181],"recent":[25],"years,":[26],"achieving":[27],"a":[28,40,59,136,156],"suitable":[29],"trade-off":[30],"between":[31],"accuracy,":[33],"inference":[34],"speed":[35],"model":[37,186],"size":[38],"remains":[39],"fundamental":[41],"challenging":[43],"issue":[44],"for":[45,51],"real-time":[46],"applications,":[49],"especially":[50],"rural":[52,67,79,162,218],"roads.":[53],"For":[54],"this":[55],"purpose,":[56],"we":[57,81,134,154],"lightweight":[60],"dynamic":[61,137],"addition":[62],"network":[63],"(LDANet)":[64],"to":[65,91,114,128,213],"exploit":[66],"extraction.":[69],"Specifically,":[70],"considering":[71],"the":[72,93,97,102,107,116,120,130,145,166,192,195,205],"narrow,":[73],"complex":[74],"diverse":[76],"nature":[77],"of":[78,119,161],"roads,":[80],"introduce":[82],"improved":[84],"Asymmetric":[85],"Convolution":[86],"Block":[87],"(ACB)-based":[88],"Inception":[89],"structure":[90],"extend":[92],"low-level":[94],"features":[95],"feature":[98,104],"layer.":[100],"In":[101,152],"deep":[103],"association":[105],"module,":[106],"depth-wise":[108],"separable":[109],"convolution":[110],"(DSC)":[111],"is":[112,126],"introduced":[113],"reduce":[115],"computational":[117],"complexity":[118],"model,":[121],"adaptation-weighted":[124],"overlay":[125],"designed":[127],"capture":[129],"salient":[131],"features.":[132],"Moreover,":[133],"utilize":[135],"weighted":[138],"combined":[139],"loss,":[140],"which":[141],"can":[142],"better":[143],"solve":[144],"sample":[146],"imbalance":[147],"boosts":[149],"segmentation":[150],"accuracy.":[151],"addition,":[153],"constructed":[155],"typical":[157],"dataset":[160],"roads":[163,219],"based":[164],"Deep":[167],"Globe":[168],"Land":[169],"Cover":[170],"Classification":[171],"Challenge":[172],"dataset.":[173],"Our":[174],"experiments":[175],"demonstrate":[176],"that":[177,191],"LDANet":[178,210],"performs":[179],"well":[180],"with":[184],"fewer":[185],"parameters":[187],"(&lt;1":[188],"MB)":[189],"accuracy":[193],"mean":[196],"Intersection":[197],"over":[198],"Union":[199],"reach":[200],"98.74%":[201],"76.21%":[203],"test":[206],"dataset,":[207],"respectively.":[208],"Therefore,":[209],"potential":[212],"rapidly":[214],"extract":[215],"monitor":[217],"images.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2023-03-31T00:00:00"}
