[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82952-en":3,"doc-seo-82952-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82952,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","GAIA: Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction","GAIA (Geometry-Aware Infrastructure-Anchored Denoiser) targets accurate work-zone geometry perception using low-cost UWB sensing. Outdoor UWB ranging suffers from NLOS propagation, burst noise, and long-tail errors that can break downstream boundary reconstruction. GAIA formulates denoising as geometry-consistent distance estimation by coupling temporal range modeling with latent anchor-layout inference and deterministic distance projection. Evaluations use real outdoor UWB with synchronized GNSS and IMU plus a real-data-calibrated stress-test simulator, improving range MSE and polygon IoU while enhancing boundary-level robustness under severe corruption.","Highlights  \nGAIA: Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and WorkZone Reconstruction  \nWeizhe Tang, Jiaxi Liu, Junwei You, Steven T. Parker, Pei Li, Sikai Chen, Meng Ran, Bin Ran  \n• Geometry-aware work-zone reconstruction: UWB mapping is formulated as boundary reconstruction, with range denoising serving the geometric objective.  \n• Latent geometry-guided denoising: A latent anchor layout is inferred and fed back into denoising as an explicit spatial prior.  \n• Real-data-centered validation with stress testing: GAIA is evaluated primarily on real outdoor UWB measurements, with a real-data-calibrated simulator used as a supplementary stress-test environment for severe NLOS and long-tail errors.  \narXiv :2607 .05449v 1 [ cs .LG] 5 Jul 2026  \nGAIA: Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction  \nWeizhe Tanga , Jiaxi Liua,∗ , Junwei Youa , Steven T. Parkera , Pei Lib , Sikai Chena , Meng Ranc and Bin Rana  \na Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, 53706, Wisconsin, United States b Department of Civil and Architectural Engineering and Construction Management, University of Wyoming, Laramie, 82071, Wyoming, United States  \nc School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China  \nARTICLE INFO  \nKeywords:  \nUWB (Ultra-wideband) Work zone reconstruction Infrastructure sensing Geometry-aware learning  \nAB STRACT  \nAccurate perception of work-zone geometry is critical for intelligent transportation systems, and ultra-wideband (UWB) sensing offers a low-cost path toward infrastructure-aided workzone reconstruction. However, UWB ranging in outdoor work zones is often affected by non-line-of-sight (NLOS) propagation, burst noise, and long-tail errors, which can severely distort downstream spatial reconstruction. Existing approaches mainly address signal-level range denoising and do not explicitly model the geometric structure needed for reliable boundary reconstruction.  \nIn this work, we present GAIA, a geometry-aware, infrastructure-anchored learning framework for UWB denoising and work-zone reconstruction. GAIA estimates geometry-consistent UWB distances by coupling temporal range modeling with latent anchor-layout estimation and deterministic distance projection. The framework keeps range denoising as the supervised prediction task while orienting the learned distances toward boundary-consistent work-zone reconstruction.  \nWe evaluate GAIA primarily on a real-world outdoor UWB dataset with synchronized UWB, GNSS, and IMU measurements under LOS and NLOS conditions. To complement this evaluation, we use a real-data-calibrated stress-test simulator to examine robustness under stronger NLOS corruption and long-tail ranging errors. On the real-world dataset, GAIA achieves the lowest overall range MSE and the highest polygon IoU among the evaluated filtering-based and learning-based baselines, reducing overall MSE by 18.4% and improving polygon IoU by 15.5% over PoseMLP. Supplementary stress-test simulation and ablation results further show that the geometry-aware components improve boundary-level reconstruction under severe ranging noise. These results indicate that geometry-aware range denoising is a promising direction for infrastructure-aided work-zone reconstruction, and that GAIA provides a boundaryoriented UWB reconstruction framework that links range correction to spatially coherent workzone geometry estimation.  \n1. Introduction  \nWork zones are among the most hazardous and geometrically dynamic environments on public roads. In 2023 alone, work-zone crashes caused 898 fatalities and more than 40,000 injuries in the U.S. National Safety Council (2024) . Work zones introduce rapidly changing drivable boundaries through lane narrowing, lateral shifts, tapers, and temporary barriers, creating geometric conditions that differ substantially from no","cbCaivQWNZXxRuZ7","https://ap.wps.com/l/cbCaivQWNZXxRuZ7","pdf",1176335,3,1,21,"English","en",105,"# Introduction\n## Work zones as hazardous, geometrically dynamic environments\n## Geometry reconstruction as the core perception task\n## Limitations of existing detection and sensing approaches","[{\"question\":\"What performance improvements does GAIA achieve over baselines?\",\"answer\":\"On the real-world dataset, GAIA achieves the lowest overall range MSE and the highest polygon IoU among evaluated baselines, reducing overall MSE by 18.4% and improving polygon IoU by 15.5% over PoseMLP. Supplementary stress testing and ablation results show better boundary-level reconstruction under severe ranging noise.\"}]",1784184299,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"gaia-geometry-aware-infrastructure-anchored-denoiser-for-uwb-sensing-and-work-zone-reconstruction","",{"@graph":36,"@context":77},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/gaia-geometry-aware-infrastructure-anchored-denoiser-for-uwb-sensing-and-work-zone-reconstruction/82952/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What performance improvements does GAIA achieve over baselines?","Question",{"text":75,"@type":76},"On the real-world dataset, GAIA achieves the lowest overall range MSE and the highest polygon IoU among evaluated baselines, reducing overall MSE by 18.4% and improving polygon IoU by 15.5% over PoseMLP. Supplementary stress testing and ablation results show better boundary-level reconstruction under severe ranging noise.","Answer","https://schema.org",{"og:url":51,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]