{"id":"https://openalex.org/W4407720886","doi":"https://doi.org/10.1109/dsit61374.2024.10882089","title":"Enhancing Autonomous Drone Semantic Segmentation for Safe Landing","display_name":"Enhancing Autonomous Drone Semantic Segmentation for Safe Landing","publication_year":2024,"publication_date":"2024-12-20","ids":{"openalex":"https://openalex.org/W4407720886","doi":"https://doi.org/10.1109/dsit61374.2024.10882089"},"language":"en","primary_location":{"id":"doi:10.1109/dsit61374.2024.10882089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsit61374.2024.10882089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Data Science and Information Technology (DSIT)","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/A5102571100","display_name":"Yuanjing Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I1284584826","display_name":"Social Science Research Council","ror":"https://ror.org/006cvnv84","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1284584826"]},{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuanjing Zhu","raw_affiliation_strings":["Duke University,Social Science Research Institute,Durham,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University,Social Science Research Institute,Durham,United States","institution_ids":["https://openalex.org/I1284584826","https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009167120","display_name":"Yuhan Liao","orcid":"https://orcid.org/0009-0007-4624-646X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuhan Liao","raw_affiliation_strings":["Nanjing Glaway Software Co., LTD,Technology Development Department,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Glaway Software Co., LTD,Technology Development Department,Nanjing,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101792310","display_name":"Yunan Liu","orcid":"https://orcid.org/0000-0002-7344-8645"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunan Liu","raw_affiliation_strings":["Duke University,Electrical & Computer Engineering,Durham,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University,Electrical & Computer Engineering,Durham,United States","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2187,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55813359,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.992900013923645,"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"}},"topics":[{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.992900013923645,"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"}},{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.975600004196167,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9722999930381775,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/drone","display_name":"Drone","score":0.8705106377601624},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6579008102416992},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5314230918884277},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4282737970352173},{"id":"https://openalex.org/keywords/aeronautics","display_name":"Aeronautics","score":0.41837847232818604},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.38352227210998535},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34813961386680603},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1755906641483307}],"concepts":[{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.8705106377601624},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6579008102416992},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5314230918884277},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4282737970352173},{"id":"https://openalex.org/C178802073","wikidata":"https://www.wikidata.org/wiki/Q8421","display_name":"Aeronautics","level":1,"score":0.41837847232818604},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.38352227210998535},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34813961386680603},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1755906641483307},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsit61374.2024.10882089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsit61374.2024.10882089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Data Science and Information Technology (DSIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W2112919533","https://openalex.org/W2124592697","https://openalex.org/W2963881378","https://openalex.org/W2992060177","https://openalex.org/W3117049164","https://openalex.org/W4308621049","https://openalex.org/W4315651971","https://openalex.org/W4401213214","https://openalex.org/W4402715945","https://openalex.org/W6750469568","https://openalex.org/W6769331050"],"related_works":["https://openalex.org/W4229448053","https://openalex.org/W4247925126","https://openalex.org/W4327774218","https://openalex.org/W2059768187","https://openalex.org/W4312858960","https://openalex.org/W4386036939","https://openalex.org/W4379143281","https://openalex.org/W2605096541","https://openalex.org/W3200286695","https://openalex.org/W4212885606"],"abstract_inverted_index":{"Ensuring":[0],"safe":[1,156],"and":[2,52,61,105,136,162],"accurate":[3],"drone":[4,15,149],"landings":[5,157],"is":[6],"a":[7,20,58,80,92,106,137],"critical":[8,114],"challenge":[9],"in":[10],"the":[11,113,119,145],"development":[12],"of":[13,32,41,83,100,110],"autonomous":[14,65],"systems.":[16],"This":[17,142],"study":[18,143],"presents":[19],"two-fold":[21],"approach":[22],"combining":[23],"granular":[24],"semantic":[25,77],"segmentation":[26,40,78,104],"with":[27,123],"high-level":[28],"binary":[29,107],"safety":[30],"classification":[31],"aerial":[33,84],"imagery.":[34],"Our":[35,89],"method":[36],"first":[37],"performs":[38],"detailed":[39,103],"environmental":[42,160],"elements,":[43],"then":[44],"categorizes":[45],"these":[46],"segments":[47],"into":[48],"\"safe":[49],"to":[50,54,75],"land\"":[51,55],"\"unsafe":[53],"zones,":[56],"providing":[57],"more":[59,138],"comprehensive":[60],"practical":[62],"solution":[63],"for":[64,102,112,147,154],"landing":[66,116],"decisions.":[67],"We":[68],"employed":[69],"three":[70],"state-of-the-art":[71],"deep":[72],"learning":[73],"algorithms":[74],"perform":[76],"on":[79],"newly-released":[81],"dataset":[82],"footage":[85],"from":[86],"residential":[87],"neighborhoods.":[88],"models":[90,120],"achieved":[91],"multiclass":[93],"Mean":[94],"Intersection":[95],"over":[96],"Union":[97],"(mIoU)":[98],"score":[99,109],"0.82":[101],"mIoU":[108],"0.90":[111],"safe/unsafe":[115],"classification.":[117,163],"Among":[118],"tested,":[121],"U-Net":[122],"MobileNetV3":[124],"encoder":[125],"stood":[126],"out":[127],"by":[128],"its":[129,152],"better":[130],"generalization":[131],"performance,":[132],"faster":[133],"training":[134],"times,":[135],"compact":[139],"model":[140],"size.":[141],"highlights":[144],"potential":[146],"real-world":[148],"applications,":[150],"emphasizing":[151],"suitability":[153],"ensuring":[155],"through":[158],"enhanced":[159],"mapping":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}