{"id":"https://openalex.org/W4413017832","doi":"https://doi.org/10.1109/iv64158.2025.11097687","title":"Self-Supervised Pretraining for Aerial Road Extraction","display_name":"Self-Supervised Pretraining for Aerial Road Extraction","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4413017832","doi":"https://doi.org/10.1109/iv64158.2025.11097687"},"language":"en","primary_location":{"id":"doi:10.1109/iv64158.2025.11097687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv64158.2025.11097687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Intelligent Vehicles Symposium (IV)","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/A5091786408","display_name":"Rupert Polley","orcid":null},"institutions":[{"id":"https://openalex.org/I143379178","display_name":"FZI Research Center for Information Technology","ror":"https://ror.org/04kdh6x72","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I143379178"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Rupert Polley","raw_affiliation_strings":["FZI Research Center for Information Technology,Karlsruhe,Germany,76131"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"FZI Research Center for Information Technology,Karlsruhe,Germany,76131","institution_ids":["https://openalex.org/I143379178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119232243","display_name":"Sai Vignesh Abishek Deenadayalan","orcid":null},"institutions":[{"id":"https://openalex.org/I143379178","display_name":"FZI Research Center for Information Technology","ror":"https://ror.org/04kdh6x72","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I143379178"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sai V.A. Deenadayalan","raw_affiliation_strings":["FZI Research Center for Information Technology,Karlsruhe,Germany,76131"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"FZI Research Center for Information Technology,Karlsruhe,Germany,76131","institution_ids":["https://openalex.org/I143379178"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060028048","display_name":"J. Marius Z\u00f6llner","orcid":"https://orcid.org/0000-0001-6190-7202"},"institutions":[{"id":"https://openalex.org/I143379178","display_name":"FZI Research Center for Information Technology","ror":"https://ror.org/04kdh6x72","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I143379178"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"J. Marius Z\u00f6llner","raw_affiliation_strings":["FZI Research Center for Information Technology,Karlsruhe,Germany,76131"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"FZI Research Center for Information Technology,Karlsruhe,Germany,76131","institution_ids":["https://openalex.org/I143379178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I143379178"],"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":"882","last_page":"888"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9700999855995178,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9700999855995178,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9634000062942505,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9340999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6202829480171204},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.5706176161766052},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.536285400390625},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42401552200317383},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4117997884750366},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.07720303535461426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6202829480171204},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.5706176161766052},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.536285400390625},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42401552200317383},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4117997884750366},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.07720303535461426},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv64158.2025.11097687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv64158.2025.11097687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320336060","display_name":"Bundesministerium f\u00fcr Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2108598243","https://openalex.org/W2464204616","https://openalex.org/W2469938794","https://openalex.org/W2559545830","https://openalex.org/W2737129951","https://openalex.org/W2798786289","https://openalex.org/W2804199516","https://openalex.org/W2811199523","https://openalex.org/W2893801697","https://openalex.org/W2903109798","https://openalex.org/W2903977688","https://openalex.org/W2963420272","https://openalex.org/W2963958441","https://openalex.org/W2965380095","https://openalex.org/W2974373385","https://openalex.org/W2975194617","https://openalex.org/W2987185654","https://openalex.org/W2995461960","https://openalex.org/W3007268491","https://openalex.org/W3195858154","https://openalex.org/W3199003182","https://openalex.org/W3200935312","https://openalex.org/W3203699578","https://openalex.org/W4309146000","https://openalex.org/W4313156423","https://openalex.org/W4320731732","https://openalex.org/W4386076493","https://openalex.org/W6754683619","https://openalex.org/W6863198155"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"for":[3,69,108],"aerial":[4,15,60,109],"image":[5,110],"segmentation":[6,38,97],"require":[7],"large":[8],"amounts":[9],"of":[10],"labeled":[11,44],"data,":[12],"but":[13],"high-quality":[14],"datasets":[16],"with":[17],"precise":[18],"annotations":[19],"are":[20],"scarce":[21],"and":[22,81,87],"costly":[23],"to":[24,55,78,84],"produce.":[25],"To":[26],"address":[27],"this":[28],"limitation,":[29],"we":[30],"propose":[31],"a":[32,105],"self-supervised":[33],"pretraining":[34,94],"method":[35,73],"that":[36,92],"improves":[37,74],"performance":[39],"while":[40],"reducing":[41],"reliance":[42],"on":[43],"data.":[45],"Our":[46],"approach":[47],"uses":[48],"inpainting-based":[49],"pretraining,":[50],"where":[51],"the":[52],"model":[53,85],"learns":[54],"reconstruct":[56],"missing":[57],"regions":[58],"in":[59,100],"images,":[61],"capturing":[62],"their":[63],"inherent":[64],"structure":[65],"before":[66],"being":[67],"fine-tuned":[68],"road":[70],"extraction.":[71],"This":[72],"generalization,":[75],"enhances":[76],"robustness":[77],"domain":[79],"shifts,":[80],"is":[82],"invariant":[83],"architecture":[86],"dataset":[88],"choice.":[89],"Experiments":[90],"show":[91],"our":[93],"significantly":[95],"boosts":[96],"accuracy,":[98],"especially":[99],"low-data":[101],"regimes,":[102],"making":[103],"it":[104],"scalable":[106],"solution":[107],"analysis.":[111]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}