LongFly: Long-Horizon UAV Vision-and-Language Navigation with Spatiotemporal Context Integration

• URL: http://arxiv.org/abs/2512.22010v1
• Date: Fri, 26 Dec 2025 12:09:40 GMT
• Title: LongFly: Long-Horizon UAV Vision-and-Language Navigation with Spatiotemporal Context Integration
• Abstract summary: LongFly is a history-awaretemporal modeling framework for long-horizon UAV VLN.<n>It transforms fragmented and redundant historical data into structured, compact, and expressive representations.<n>It outperforms state-of-the-art UAV VLN baselines by 7.89% in success rate and 6.33% in success weighted by path length.
• Score: 49.676794850174325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned aerial vehicles (UAVs) are crucial tools for post-disaster search and rescue, facing challenges such as high information density, rapid changes in viewpoint, and dynamic structures, especially in long-horizon navigation. However, current UAV vision-and-language navigation(VLN) methods struggle to model long-horizon spatiotemporal context in complex environments, resulting in inaccurate semantic alignment and unstable path planning. To this end, we propose LongFly, a spatiotemporal context modeling framework for long-horizon UAV VLN. LongFly proposes a history-aware spatiotemporal modeling strategy that transforms fragmented and redundant historical data into structured, compact, and expressive representations. First, we propose the slot-based historical image compression module, which dynamically distills multi-view historical observations into fixed-length contextual representations. Then, the spatiotemporal trajectory encoding module is introduced to capture the temporal dynamics and spatial structure of UAV trajectories. Finally, to integrate existing spatiotemporal context with current observations, we design the prompt-guided multimodal integration module to support time-based reasoning and robust waypoint prediction. Experimental results demonstrate that LongFly outperforms state-of-the-art UAV VLN baselines by 7.89\% in success rate and 6.33\% in success weighted by path length, consistently across both seen and unseen environments.

Related papers

• From Semantic Grounding to Decision Optimization: A Unified Framework for Long-Horizon UAV Vision-Language Navigation [40.51712647240084]
  UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments.<n>Current approaches suffer from three coupled issues: weak grounding of instruction-relevant landmarks in visual observations, insufficient exploitation of long-horizon history, and unstable decisions under local traps or repeated exploration.<n>We propose a unified semantic-to-decision framework to address these issues.
  arXiv  Detail & Related papers  (2026-08-10T12:59:50Z)
• Efficient Spatio-Temporal Grounding with Multimodal Large Models via Second-Level Tracking and RL Verification [11.171207895220578]
  temporal-temporal grounding in long videos requires precise temporal localization and robust object tracking conditioned on natural-language queries.<n>We propose a practical pipeline that shifts from frame-level to second-level tracking and performs cross-second annotations to preserve continuity while reducing sequence length.
  arXiv  Detail & Related papers  (2026-06-27T17:42:32Z)
• Think Like a Pilot: Fine-Grained Long-Horizon UAV Navigation [22.062997228195815]
  Language-guided UAV agents must execute long-horizon semantic instructions while producing smooth, physically feasible continuous flight commands.<n>We introduce textbfFLIGHT, a textbfFine-grained textbfLong-horizon textbfInstruction-textbfGuided benchmark for textbfHybrid UAV navigation and reasoning textbfTasks.<n>To endow the UAV agent with the capability of real-time in-flight reasoning over task execution status and mission planning, we
  arXiv  Detail & Related papers  (2026-06-05T02:23:05Z)
• ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model [53.15040805435013]
  Vision-language models (VLMs) provide strong semantic grounding and general knowledge by reasoning over uniformly sampled frames.<n>We propose a VLM-guided JEPA-style latent world modeling framework that combines dense-frame dynamics modeling with long-horizon semantic guidance.
  arXiv  Detail & Related papers  (2026-03-23T17:59:42Z)
• Aerial World Model for Long-horizon Visual Generation and Navigation in 3D Space [48.19308247102762]
  We propose ANWM, an aerial navigation world model that predicts future visual observations conditioned on past frames and actions.<n> ANWM is trained on 4-DoF UAV trajectories and introduces a physics-inspired module: Future Frame Projection.<n> Empirical results demonstrate that ANWM significantly outperforms existing world models in long-distance visual forecasting and improves UAV navigation success rates in large-scale environments.
  arXiv  Detail & Related papers  (2025-12-26T06:22:39Z)
• History-Enhanced Two-Stage Transformer for Aerial Vision-and-Language Navigation [64.51891404034164]
  Aerial Vision-and-Language Navigation (AVLN) requires Unmanned Aerial Vehicle (UAV) agents to localize targets in large-scale urban environments.<n>Existing UAV agents typically adopt mono-granularity frameworks that struggle to balance these two aspects.<n>This work proposes a History-Enhanced Two-Stage Transformer (HETT) framework, which integrates the two aspects through a coarse-to-fine navigation pipeline.
  arXiv  Detail & Related papers  (2025-12-16T09:16:07Z)
• DARTs: A Dual-Path Robust Framework for Anomaly Detection in High-Dimensional Multivariate Time Series [22.29889788385778]
  Multi-dimensional time series anomaly (MTSAD) aims to accurately identify and localize complex abnormal patterns in large-scale industrial control systems.<n>Existing approaches excel in recognizing distinct patterns under low representations, but fail to robustly capture long-range dependencies when learning from the high-dimensional time series.
  arXiv  Detail & Related papers  (2025-12-14T07:40:23Z)
• Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
  Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
  arXiv  Detail & Related papers  (2025-10-28T10:05:53Z)
• Leveraging Multivariate Long-Term History Representation for Time Series Forecasting [6.661358934189792]
  We propose a framework called Long-term Multivariate Representation (LMHR) for MTS forecasting.<n>LMHR encodes the long-term history into segment-level contextual representations and reduces point-level noise.<n>It consistently improves prediction accuracy by 9.8% on the top 10% of rapidly changing patterns.
  arXiv  Detail & Related papers  (2025-05-20T03:46:36Z)
• Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation [51.2732688481343]
  We introduce Longtextbf-RVOS, a large-scale benchmark for long-term referring object segmentation.<n>Long-RVOS contains 2,000+ videos of an average duration exceeding 60 seconds, covering a variety of objects.<n>Unlike previous benchmarks that rely solely on the per-frame spatial evaluation, we introduce two metrics to assess the temporal andtemporal consistency.
  arXiv  Detail & Related papers  (2025-05-19T04:52:31Z)
• Multi-step Temporal Modeling for UAV Tracking [14.687636301587045]
  We introduce MT-Track, a streamlined and efficient multi-step temporal modeling framework for enhanced UAV tracking. We unveil a unique temporal correlation module that dynamically assesses the interplay between the template and search region features. We propose a mutual transformer module to refine the correlation maps of historical and current frames by modeling the temporal knowledge in the tracking sequence.
  arXiv  Detail & Related papers  (2024-03-07T09:48:13Z)
• LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry [53.5449912019877]
  We present the Long-term Effective Any Point Tracking (LEAP) module.<n>LEAP innovatively combines visual, inter-track, and temporal cues with mindfully selected anchors for dynamic track estimation.<n>Based on these traits, we develop LEAP-VO, a robust visual odometry system adept at handling occlusions and dynamic scenes.
  arXiv  Detail & Related papers  (2024-01-03T18:57:27Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation [23.645412918420906]
  Unsupervised Video Object (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. Previous methods do not fully use spatial-temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view.
  arXiv  Detail & Related papers  (2023-09-21T01:09:46Z)
• Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction [10.3426659705376]
  We propose a novel dynamic graph convolution network with attention fusion to model synchronous spatial-temporal correlations. We conduct extensive experiments in four real-world traffic datasets to demonstrate that our method surpasses state-of-the-art performance compared to 18 baseline methods.
  arXiv  Detail & Related papers  (2023-02-24T12:21:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.