[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86499-en":3,"doc-seo-86499-105":30,"detail-sidebar-cat-0-en-105":91},{"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},86499,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Benchmarking UAV-based Vehicle Re-Identification under Simulated Weather Conditions","UAV-based vehicle re-identification (ReID) supports traffic surveillance, urban monitoring, and public-safety use cases through flexible viewpoints and wide-area coverage. Existing UAV ReID benchmarks provide limited evidence on robustness when adverse weather degrades fine-grained appearance cues, especially for small objects, large viewpoint shifts, and complex backgrounds. This work performs a controlled comparison of CLIP-ReID, MSINet, and AdaSP on VRU and UAV-VeID by generating synthetic foggy and rainy variants with a weather-effect pipeline that preserves identities and splits. Results show consistent retrieval degradation under adverse weather, with rain causing larger mAP drops than fog. AdaSP achieves the strongest robustness.","Benchmarking UAV-based Vehicle Re-Identification under Simulated Weather Conditions  \n1st Vu Minh Tran University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh City, Vietnam [23521819@gm.uit.edu.vn](23521819@gm.uit.edu.vn)  \n2nd Khang Nguyen*  \nUniversity of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh City, Vietnam [khangnttm@uit.edu.vn](khangnttm@uit.edu.vn)  \narXiv :2607 . 10583v1 [ cs .CV] 12 Jul 2026  \nAbstract—UAV-based vehicle re-identification (ReID) has emerged as a promising technique for traffic surveillance, urban monitoring, and public-safety applications thanks to the flexible viewpoints and wide-area coverage provided by unmanned aerial vehicles. However, despite recent progress on UAV-based vehicle ReID benchmarks, the robustness of existing methods under adverse weather remains insufficiently studied. This is important because weather degradation can significantly affect the finegrained appearance cues required for reliable vehicle matching in aerial imagery, especially under small object scale, viewpoint variation, and complex backgrounds. In this paper, we present a controlled comparative study of three representative recent vehicle ReID methods, namely CLIP-ReID, MSINet, and AdaSP, on two UAV-based benchmarks, VRU and UAV-VeID. To ensure consistent robustness evaluation, we generate synthetic foggy and rainy variants of both datasets using an analytical weather-effect pipeline while preserving the original identities and data splits. All methods are then trained and evaluated under matched clean, foggy, and rainy conditions. Experimental results show that adverse weather consistently degrades retrieval performance across both datasets, with rain causing larger drops than fog in nearly all settings. Among the evaluated methods, AdaSP demonstrates the strongest robustness, achieving 93.0% and 88.5% mAP on VRU-Large, and 88.7% and 76.2% mAP on UAVVeID-Test under foggy and rainy conditions, respectively. Overall, our findings show that simulated adverse weather substantially increases the difficulty of UAV-based vehicle ReID, reveals clear robustness differences among recent methods, and highlights the need for weather-aware model design and evaluation protocols in future aerial ReID research. The code is released at [https://github.com/tranminhvu945/Benchmarking-ReID](https://github.com/tranminhvu945/Benchmarking-ReID).  \nIndex Terms—Vehicle Re-Identification, UAVs, adverse weather.  \nI. INTRODUCTION  \nVehicle re-identification (ReID) aims to match the same vehicle across different images or camera views and has become an important research topic in computer vision due to its applications in intelligent transportation, large-scale surveillance, traffic analysis, and public safety [1] . As illustrated in Fig. 1, a typical vehicle ReID system takes a query image, compares it against a gallery set in a learned feature space, and returns a ranked list of candidate matches according to similarity scores. In recent years, unmanned aerial vehicles  \n(UAVs) have expanded the scope of this problem by enabling * Corresponding author: Khang Nguyen, [khangnttm@uit.edu.vn](khangnttm@uit.edu.vn).  \nimage acquisition from flexible viewpoints, varying altitudes, and dynamic trajectories. Compared with conventional fixedcamera settings, UAV-based vehicle ReID is inherently more challenging because the same vehicle may appear under large viewpoint shifts, scale variation, background clutter, motion blur, and unstable observation conditions [2]–[4] . These challenges are also consistent with observations reported in recent UAV-oriented re-identification surveys [5] .  \nDespite substantial progress in vehicle ReID under clean conditions, practical UAV deployment must also consider environmental corruption. Among these factors, adverse weather is particularly important. Fog reduces visibility and contrast, while rain introduces streaks, blur, and appearance di","cbCaisiUL6pJcdZS","https://ap.wps.com/l/cbCaisiUL6pJcdZS","pdf",1693277,4,1,6,"English","en",105,"# Introduction\n## Vehicle Re-Identification and UAV Challenges\n## Adverse Weather Impact\n## Motivation and Paper Contributions","[{\"question\":\"What problem does the paper focus on in UAV-based vehicle re-identification?\",\"answer\":\"The paper focuses on how robust UAV-based vehicle ReID methods are under adverse weather, since fog and rain can damage fine-grained appearance cues in aerial imagery.\"},{\"question\":\"How are foggy and rainy conditions created for evaluation?\",\"answer\":\"Foggy and rainy dataset variants are generated using an analytical weather-effect generation pipeline that preserves the original identities and data splits.\"},{\"question\":\"Which method shows the best robustness under foggy and rainy conditions?\",\"answer\":\"AdaSP shows the strongest robustness, achieving the highest mAP in the reported foggy and rainy evaluations on VRU-Large and UAV-VeID-Test.\"}]",1784212209,15,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"benchmarking-uav-based-vehicle-re-identification-under-simulated-weather-conditions","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"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":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/benchmarking-uav-based-vehicle-re-identification-under-simulated-weather-conditions/86499/",{"url":52,"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","2026-07-27","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper focus on in UAV-based vehicle re-identification?","Question",{"text":75,"@type":76},"The paper focuses on how robust UAV-based vehicle ReID methods are under adverse weather, since fog and rain can damage fine-grained appearance cues in aerial imagery.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are foggy and rainy conditions created for evaluation?",{"text":80,"@type":76},"Foggy and rainy dataset variants are generated using an analytical weather-effect generation pipeline that preserves the original identities and data splits.",{"name":82,"@type":73,"acceptedAnswer":83},"Which method shows the best robustness under foggy and rainy conditions?",{"text":84,"@type":76},"AdaSP shows the strongest robustness, achieving the highest mAP in the reported foggy and rainy evaluations on VRU-Large and 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