{"id":"https://openalex.org/W7162517052","doi":"https://doi.org/10.48550/arxiv.2605.26862","title":"RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction","display_name":"RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162517052","doi":"https://doi.org/10.48550/arxiv.2605.26862"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.26862","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26862","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.26862","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137126036","display_name":"Chenxu Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Chenxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137184597","display_name":"Chenxu Wang","orcid":"https://orcid.org/0000-0002-9266-7333"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chenxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101658016","display_name":"Yimian Dai","orcid":"https://orcid.org/0000-0003-1052-2489"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Yimian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137187385","display_name":"Yongxiang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yongxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037131575","display_name":"Ming\u2010Ming Cheng","orcid":"https://orcid.org/0000-0001-5550-8758"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Ming-Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137180359","display_name":"Xiang Li","orcid":"https://orcid.org/0000-0003-1253-9137"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9961000084877014,"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":0.9961000084877014,"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":0.0007999999797903001,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.00039999998989515007,"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/segmentation","display_name":"Segmentation","score":0.7074000239372253},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6588000059127808},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6452000141143799},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.6111999750137329},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4675999879837036},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4474000036716461},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.44119998812675476},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.43369999527931213}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7581999897956848},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7074000239372253},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6588000059127808},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6452000141143799},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.6111999750137329},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4675999879837036},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4474000036716461},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.44119998812675476},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43650001287460327},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.39239999651908875},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3560999929904938},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35499998927116394},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.3544999957084656},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35019999742507935},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.26862","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26862","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.26862","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26862","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"road":[1,45,89,101,115,142],"segmentation":[2,46,176],"from":[3,17,55],"aerial":[4],"imagery":[5],"is":[6],"fundamental":[7],"to":[8,48,87],"many":[9],"geospatial":[10],"applications.":[11],"However,":[12],"existing":[13],"datasets":[14],"often":[15,85],"suffer":[16],"limited":[18],"scene":[19],"diversity,":[20],"low":[21],"semantic":[22],"granularity,":[23],"and":[24,42,58,64,72,79,133,148,161,178,183],"poor":[25],"structural":[26,146],"continuity,":[27],"restricting":[28],"their":[29],"generalization":[30],"across":[31,61],"environments.":[32],"To":[33,103,144],"address":[34],"these":[35],"challenges,":[36],"we":[37,106],"introduce":[38],"WorldRoadSeg-360K,":[39],"the":[40,139,163],"largest":[41],"most":[43],"diverse":[44,78],"dataset":[47],"date,":[49],"comprising":[50],"366,947":[51],"high-resolution":[52],"images":[53],"collected":[54],"38":[56],"countries":[57],"223":[59],"cities":[60],"various":[62],"terrains":[63],"continents.":[65],"WorldRoadSeg-360K":[66,182],"serves":[67],"as":[68],"a":[69,110],"comprehensive":[70],"benchmark":[71],"reveals":[73],"key":[74],"challenges":[75],"in":[76,117,174],"handling":[77],"structurally":[80],"complex":[81],"scenes.":[82],"Automated":[83],"approaches":[84],"struggle":[86],"preserve":[88],"connectivity,":[90],"while":[91,186],"current":[92],"interactive":[93,112,168],"methods":[94],"lack":[95],"efficient,":[96],"topology-sensitive":[97],"tools":[98],"for":[99,114,167],"real-world":[100],"editing.":[102],"this":[104],"end,":[105],"present":[107],"RoadGIE,":[108],"establishing":[109],"novel":[111],"paradigm":[113],"extraction":[116],"remote":[118],"sensing.":[119],"Unlike":[120],"prior":[121],"point-":[122],"or":[123],"box-based":[124],"prompting":[125,159],"strategies,":[126],"RoadGIE":[127,155,170],"supports":[128],"connectivity-aware":[129],"prompts,":[130],"including":[131],"clicks":[132],"scribbles,":[134],"which":[135],"inherently":[136],"align":[137],"with":[138,190],"topology":[140],"of":[141],"networks.":[143],"improve":[145],"consistency":[147,180],"mitigate":[149],"performance":[150,173],"degradation":[151],"during":[152],"iterative":[153],"interactions,":[154],"integrates":[156],"an":[157],"expert-guided":[158],"strategy":[160],"adapts":[162],"skeleton-based":[164],"recall":[165],"loss":[166],"scenarios.":[169],"achieves":[171],"state-of-the-art":[172],"both":[175],"accuracy":[177],"topological":[179],"on":[181],"other":[184],"benchmarks,":[185],"maintaining":[187],"efficient":[188],"operation":[189],"only":[191],"3.7M":[192],"parameters.":[193],"The":[194],"code":[195],"are":[196],"publicly":[197],"available":[198],"at:":[199],"https://github.com/chaineypung/RoadGIE":[200]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-28T00:00:00"}
