{"id":"https://openalex.org/W4412164660","doi":"https://doi.org/10.1109/aicconf64766.2025.11064201","title":"Technical principles and accuracy evaluation of UAV remote sensing technology and deep learning algorithms in land survey and detection","display_name":"Technical principles and accuracy evaluation of UAV remote sensing technology and deep learning algorithms in land survey and detection","publication_year":2025,"publication_date":"2025-06-13","ids":{"openalex":"https://openalex.org/W4412164660","doi":"https://doi.org/10.1109/aicconf64766.2025.11064201"},"language":"en","primary_location":{"id":"doi:10.1109/aicconf64766.2025.11064201","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aicconf64766.2025.11064201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 3rd Cognitive Models and Artificial Intelligence Conference (AICCONF)","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/A5102669355","display_name":"Fengjiao Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210157972","display_name":"Chongqing Municipal Government","ror":"https://ror.org/05ct91r78","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210157972"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fengjiao Guo","raw_affiliation_strings":["CPC Chongqing Party School,Graduate School,Chongqing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CPC Chongqing Party School,Graduate School,Chongqing,China","institution_ids":["https://openalex.org/I4210157972"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5102669355"],"corresponding_institution_ids":["https://openalex.org/I4210157972"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14139","display_name":"E-commerce and Technology Innovations","score":0.6367999911308289,"subfield":{"id":"https://openalex.org/subfields/1403","display_name":"Business and International Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T14139","display_name":"E-commerce and Technology Innovations","score":0.6367999911308289,"subfield":{"id":"https://openalex.org/subfields/1403","display_name":"Business and International Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.6238999962806702,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.675868034362793},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4864470660686493},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45056217908859253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39367568492889404},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33155566453933716},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07973763346672058}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.675868034362793},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4864470660686493},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45056217908859253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39367568492889404},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33155566453933716},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07973763346672058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aicconf64766.2025.11064201","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aicconf64766.2025.11064201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 3rd Cognitive Models and Artificial Intelligence Conference (AICCONF)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.4399999976158142,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W4400975846","https://openalex.org/W4402729266","https://openalex.org/W6802826407","https://openalex.org/W6809040411","https://openalex.org/W6840090980","https://openalex.org/W6840294134","https://openalex.org/W6852125102","https://openalex.org/W6853385299","https://openalex.org/W6853830912","https://openalex.org/W6854316198","https://openalex.org/W6856764946","https://openalex.org/W6858179415","https://openalex.org/W6861681611","https://openalex.org/W6863109730","https://openalex.org/W6863652263","https://openalex.org/W6869370643","https://openalex.org/W6871134330","https://openalex.org/W6871148308","https://openalex.org/W6871383708","https://openalex.org/W6871613876","https://openalex.org/W6871719730","https://openalex.org/W6872708560","https://openalex.org/W6873179545","https://openalex.org/W6873469278"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"The":[0,30,44,108,148],"integration":[1,161],"of":[2,77,137,162,183,186,213],"new":[3],"changes":[4],"in":[5,14,129,209,222,242],"land":[6,69,85,214,251],"surveying":[7],"and":[8,19,28,50,80,91,105,135,164,168,174,190,216,239,253],"detection":[9,254],"methodologies":[10],"has":[11],"been":[12],"provided":[13],"Unmanned":[15],"aerial":[16],"vehicles":[17],"(UAV)":[18],"deep":[20,53,165,192],"learning":[21,54,166,193],"algorithms":[22,83],"to":[23,59,68,122,152,159,170,195,235],"acquire":[24],"data":[25,61,101,138,172],"more":[26],"precisely":[27],"accurately.":[29],"research":[31,72,231],"work":[32],"focuses":[33],"on":[34],"the":[35,75,113,123,141,160,181,184,211],"technical":[36],"aspects":[37],"that":[38,112],"support":[39],"UAV":[40,103,146,163,201],"remote":[41,202],"sensing":[42,203],"technology.":[43],"text":[45],"further":[46],"discusses":[47],"how":[48],"effectively":[49],"efficiently":[51],"various":[52],"models":[55,115,194],"can":[56],"be":[57,207],"used":[58],"process":[60],"originating":[62],"from":[63,102],"UAVs":[64],"for":[65,84,144,220,250],"issues":[66],"related":[67,158],"detection.":[70],"This":[71,177],"paper":[73],"analyzes":[74],"performance":[76],"CNNs,":[78],"U-Net,":[79],"Mask":[81],"R-CNN":[82],"feature":[86],"classification":[87],"across":[88],"urban,":[89],"agricultural,":[90],"forest":[92],"terrains.":[93],"Using":[94],"a":[95,247],"mixed-method":[96],"approach,":[97],"this":[98],"study":[99,149,178],"utilizes":[100],"LiDAR":[104],"multispectral":[106],"sensors.":[107],"results":[109],"have":[110],"proved":[111],"U-Net":[114],"achieved":[116],"higher":[117],"segmentation":[118],"accuracies":[119],"as":[120],"compared":[121],"others;":[124],"precision":[125,238],"was":[126],"almost":[127],"93%":[128],"an":[130,256],"urban":[131,228],"environment.":[132,259],"Environmental":[133],"factors":[134],"limitations":[136],"processing":[139,198],"are":[140],"major":[142],"challenges":[143,157],"optimizing":[145],"applications.":[147],"adds":[150],"value":[151],"existing":[153],"knowledge":[154],"by":[155,245],"discussing":[156],"technologies":[167,189],"strategies":[169],"enhance":[171,196],"collection":[173],"model":[175,237],"optimization.":[176],"brings":[179],"out":[180],"importance":[182],"adoption":[185],"hybrid":[187],"sensor":[188],"lightweight":[191],"real-time":[197],"capabilities.":[199],"As":[200],"progresses,":[204],"it":[205],"will":[206,233],"crucial":[208],"developing":[210],"practices":[212],"survey":[215,252],"producing":[217],"real-world":[218],"applications":[219],"professionals":[221],"geospatial":[223],"science,":[224],"environmental":[225],"monitoring,":[226],"or":[227],"planning.":[229],"Future":[230],"directions":[232],"need":[234],"improve":[236],"increase":[240],"applicability":[241],"diverse":[243],"landscapes":[244],"providing":[246],"solid":[248],"framework":[249],"within":[255],"increasingly":[257],"complex":[258]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}