Trajectory-aware Cross-view Geo-localization with Sequential Observations

• URL: http://arxiv.org/abs/2607.15491v1
• Date: Thu, 16 Jul 2026 22:39:19 GMT
• Title: Trajectory-aware Cross-view Geo-localization with Sequential Observations
• Abstract summary: Cross-view-localization matches ground-level observations against satellite imagery.<n>Recent methods show that sequential queries such as video clips yield richer cues than single images.<n>We introduce SeqLoc-VL, a dataset of $sim$39K--text triplets, and TrajLoc, a unified framework capable of processing video clips and route descriptions.
• Score: 14.771498439131953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-view geo-localization matches ground-level observations against geo-tagged satellite imagery. Recent methods show that sequential queries such as video clips yield richer spatiotemporal cues than single images, yet they overlook a complementary sequential modality: route descriptions -- which capture the same trajectory at a higher level of abstraction and are often the only input available (e.g., a user directing an autonomous vehicle to a pickup point). To bridge this gap, we introduce SeqGeo-VL, a dataset of $\sim$39K video-text-satellite triplets, and TrajLoc, a unified framework capable of processing both video clips and route descriptions. By leveraging both dense visual and abstract linguistic semantics, TrajLoc enables these modalities to mutually reinforce cross-view matching. We further propose TrajMod, a lightweight module that conditions query embeddings on trajectory geometry, yielding spatially-aware representations. Experiments show that TrajLoc achieves substantial gains over state-of-the-art methods on both video and text geo-localization. The project page is available at https://humblegamer.github.io/trajloc/.

Related papers

• GeoTrace: Geometry-Aware Trajectory Token Compression for Video Large Language Models [25.376020984792845]
  GeoTrace is a training-free token compression framework for Video Large Language Models.<n>It decomposes video evidence into exact skeleton tokens and traceable residual event tokens.<n>It achieves a (12.99times) TFLOPs reduction while preserving 99.1% of the vanilla performance.
  arXiv  Detail & Related papers  (2026-07-10T03:58:26Z)
• TrajPrism: A Multi-Task Benchmark for Language-Grounded Urban Trajectory Understanding [9.669733030377632]
  We introduce TrajPrism, a multi-task benchmark for language-trajectory alignment.<n>We construct TrajPrism by pairing real urban trajectories with judge-language annotations generated under a travel-intent taxonomy.<n>The benchmark contains 300K selected trajectories across Porto, San Francisco, and Beijing, yielding 2.1M task instances.
  arXiv  Detail & Related papers  (2026-05-11T16:17:13Z)
• VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale [46.71012068113612]
  The task of video geolocalization aims to determine the precise GPS coordinates of a video's origin and map its trajectory.<n>We propose VidTAG, a dual-encoder framework that performs frame-to-GPS retrieval using both self-supervised and language-aligned features.<n>Our approach enables fine-grained video geolocalization and lays a strong foundation for future research.
  arXiv  Detail & Related papers  (2026-04-14T00:35:07Z)
• Learning Trajectory-Aware Multimodal Large Language Models for Video Reasoning Segmentation [55.743701532984126]
  We propose TrajSeg, a simple and unified framework built upon Multimodal Large Language Models.<n>In this work, we introduce bidirectional text-trajectory alignment, where MLLMs accept grounding-intended (text-to-trajectory) and captioning-intended (trajectory-to-text) instructions.<n>This way, MLLMs can benefit from enhanced correspondence and better perceive object trajectories in videos.
  arXiv  Detail & Related papers  (2026-03-23T02:25:51Z)
• GeoBridge: A Semantic-Anchored Multi-View Foundation Model Bridging Images and Text for Geo-Localization [36.20562550716618]
  Cross-view geo-localization infers a location by retrieving geo-tagged reference images that visually correspond to a query image.<n>Traditional satellite-centric paradigm limits robustness when high-resolution or up-to-date satellite imagery is unavailable.<n>We propose GeoBridge, a model that performs bidirectional matching across views and supports language-to-image retrieval.
  arXiv  Detail & Related papers  (2025-12-02T12:28:22Z)
• From Street to Orbit: Training-Free Cross-View Retrieval via Location Semantics and LLM Guidance [10.533095161205358]
  Cross-view image retrieval is critical for applications such as autonomous navigation, urban planning, and localization in GPS-denied environments.<n>We present a simple yet effective cross-view image retrieval framework that leverages a pretrained vision encoder and a large language model (LLM)<n>Despite using no ground-truth supervision or finetuning, our proposed method outperforms prior learning-based approaches on the benchmark dataset under zero-shot settings.
  arXiv  Detail & Related papers  (2025-11-12T23:51:46Z)
• GLEAM: Learning to Match and Explain in Cross-View Geo-Localization [66.11208984986813]
  Cross-View Geo-Localization (CVGL) focuses on identifying correspondences between images captured from distinct perspectives of the same geographical location.<n>We present GLEAM-C, a foundational CVGL model that unifies multiple views and modalities-including UAV imagery, street maps, panoramic views, and ground photographs-by aligning them exclusively with satellite imagery.<n>To address the lack of interpretability in traditional CVGL methods, we propose GLEAM-X, which combines cross-view correspondence prediction with explainable reasoning.
  arXiv  Detail & Related papers  (2025-09-09T07:14:31Z)
• GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching [6.8045687415659275]
  Cross-view geo-localisation identifies coarse geographical position of an automated vehicle by matching a ground-level image to a geo-tagged satellite image from a database.<n>Existing approaches reach high recall rates but they still fail to rank the correct image as the top match.<n>This paper proposes GeoVLM, a novel approach which uses the zero-shot capabilities of vision language models to enable cross-view geo-localisation.
  arXiv  Detail & Related papers  (2025-05-19T19:17:06Z)
• CVLNet: Cross-View Semantic Correspondence Learning for Video-based Camera Localization [89.69214577915959]
  This paper tackles the problem of Cross-view Video-based camera localization. We propose estimating the query camera's relative displacement to a satellite image before similarity matching. Experiments have demonstrated the effectiveness of video-based localization over single image-based localization.
  arXiv  Detail & Related papers  (2022-08-07T07:35:17Z)
• Satellite Image Based Cross-view Localization for Autonomous Vehicle [59.72040418584396]
  This paper shows that by using an off-the-shelf high-definition satellite image as a ready-to-use map, we are able to achieve cross-view vehicle localization up to a satisfactory accuracy. Our method is validated on KITTI and Ford Multi-AV Seasonal datasets as ground view and Google Maps as the satellite view.
  arXiv  Detail & Related papers  (2022-07-27T13:16:39Z)
• GAMa: Cross-view Video Geo-localization [68.33955764543465]
  We focus on ground videos instead of images which provides contextual cues. At clip-level, a short video clip is matched with corresponding aerial image and is later used to get video-level geo-localization of a long video. Our proposed method achieves a Top-1 recall rate of 19.4% and 45.1% @1.0mile.
  arXiv  Detail & Related papers  (2022-07-06T04:25:51Z)
• Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image [91.29546868637911]
  This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method.
  arXiv  Detail & Related papers  (2022-04-10T19:16:58Z)

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.