SatNav：基于卫星影像的长时程无人机视觉-语言导航可扩展基准
SatNav: A Scalable Benchmark for Long-Horizon UAV Vision-Language Navigation from Satellite Imagery
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
SatNav利用卫星影像构建可扩展的长时程无人机视觉-语言导航基准，包含118K场景和三个任务族，并通过SwiftVLN框架及迁移实验验证了城市级导航的挑战性与实用性。
AI中文摘要：
城市无人机视觉-语言导航（VLN）要求智能体在广阔的城市空间中遵循指令，本质上需要长期记忆和地理空间定位能力。然而，现有基准的扩展因依赖成本高昂的重建3D资产而困难重重，限制了地理多样性和场景规模。为解决此问题，我们提出SatNav，一个基于高分辨率卫星影像构建的可扩展、长时程无人机VLN基准。SatNav针对城市级导航任务，使用卫星裁剪图作为无人机正下方视角的近似视觉观测。通过自动化的线索到场景生成流程，SatNav从18个城市的59个场景构建了118K个场景，平均轨迹长度为379米。为压力测试长时程记忆和地理空间推理，SatNav定义了三个任务族：边界（Boundary）、地标（Landmark）和路线（Route），分别针对环路进度跟踪、基于地标的空间定位以及带计数线索的路线跟随。在SatNav上对经典VLN智能体和基于大型视觉-语言模型（LVLMs）的最新智能体进行基准测试表明，城市规模导航仍具挑战性。我们进一步引入SwiftVLN，一个具有可切换记忆组件的模块化框架，并进行了系统的记忆设计消融实验。最后，卫星到无人机迁移实验表明，卫星训练的导航模型可操作于真实飞行无人机观测，展示了SatNav的实际应用价值。我们的项目页面：此https URL
英文摘要：
Urban uncrewed aerial vehicle (UAV) vision-language navigation (VLN) requires agents to follow instructions across extended urban spaces, inherently demanding long-term memory and geospatial grounding. However, scaling existing benchmarks remains difficult because of their reliance on costly reconstructed 3D assets, limiting geographic diversity and episode scale. To address this, we introduce SatNav, a scalable, long-horizon UAV VLN benchmark built from high-resolution satellite imagery. SatNav targets city-level navigation missions and uses satellite crops as approximations of UAV nadir views for visual observations. Through an automated cue-to-episode pipeline, SatNav constructs 118K episodes from 59 scenes across 18 cities, with an average trajectory length of 379 m. To stress-test long-horizon memory and geospatial reasoning, SatNav defines three task families: Boundary, Landmark, and Route, targeting loop progress tracking, landmark-based spatial grounding, and route following with counting cues. Benchmarking classical VLN agents and recent agents based on large vision-language models (LVLMs) on SatNav shows that city-scale navigation remains challenging. We further introduce SwiftVLN, a modular framework with switchable memory components, and conduct systematic memory-design ablations. Finally, satellite-to-UAV transfer experiments show that satellite-trained navigation models can operate on real-flight UAV observations, showing the practical relevance of SatNav. Our project page: https://eku127.github.io/SatNav/
