Related papers: Visual Geo-Localization from images

Related papers

When Vision Misleads, Let Location Speak: A Worldwide Image Geo-Localization Method via Location Attention Mechanism and Large Multimodal Models [23.448145400461513]
Existing methods often mislocalize images by matching them to visually similar scenes from different geographic regions.<n>We propose TransGeoCLIP, a novel retrieval-based framework that integrates a location attention mechanism and large multimodal models.<n>We show that TransGeoCLIP significantly enhances localization performance for visually similar images.
arXiv Detail & Related papers (2026-06-08T01:49:44Z)
Street-Level Geolocalization Using Multimodal Large Language Models and Retrieval-Augmented Generation [0.0]
Street-level geolocalization from images is crucial for a wide range of essential applications and services, such as navigation, location-based recommendations, and urban planning.<n>This paper introduces a novel approach that integrates open-weight and publicly accessible multimodal large language models with retrieval-augmented generation.
arXiv Detail & Related papers (2025-09-01T10:23:48Z)
VLM-Guided Visual Place Recognition for Planet-Scale Geo-Localization [24.433604332415204]
We propose a novel hybrid geo-localization framework that combines the strengths of vision-language models and visual place recognition.<n>We evaluate our approach on multiple geo-localization benchmarks and show that it consistently outperforms prior state-of-the-art methods.
arXiv Detail & Related papers (2025-07-23T12:23:03Z)
Pole-based Vehicle Localization with Vector Maps: A Camera-LiDAR Comparative Study [6.300346102366891]
In road environments, many common furniture such as traffic signs, traffic lights and street lights take the form of poles. This paper introduces a real-time method for camera-based pole detection using a lightweight neural network trained on automatically annotated images. The results highlight the high accuracy of the vision-based approach in open road conditions.
arXiv Detail & Related papers (2024-12-11T09:05:05Z)
Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
arXiv Detail & Related papers (2024-08-21T03:31:30Z)
G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models [40.69217368870192]
We propose a novel framework for worldwide geolocalization based on Retrieval-Augmented Generation (RAG) G3 consists of three steps, i.e., Geo-alignment, Geo-diversification, and Geo-verification. Experiments on two well-established datasets verify the superiority of G3 compared to other state-of-the-art methods.
arXiv Detail & Related papers (2024-05-23T15:37:06Z)
ConGeo: Robust Cross-view Geo-localization across Ground View Variations [34.192775134189965]
Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. Existing learning pipelines are orientation-specific or FoV-specific, demanding separate model training for different ground view variations. We propose ConGeo, a single- and cross-view Contrastive method for Geo-localization.
arXiv Detail & Related papers (2024-03-20T20:37:13Z)
GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
arXiv Detail & Related papers (2023-09-27T20:54:56Z)
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)
Co-visual pattern augmented generative transformer learning for automobile geo-localization [12.449657263683337]
Cross-view geo-localization (CVGL) aims to estimate the geographical location of the ground-level camera by matching against enormous geo-tagged aerial images. We present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL) for CVGL.
arXiv Detail & Related papers (2022-03-17T07:29:02Z)
Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors [25.87104194833264]
We propose a novel method for geo-tracking in outdoor environments by registering a vehicle's sensor information with aerial imagery of an unseen target region. We train a model in a metric learning setting to extract visual features from ground and aerial images. Our method is the first to utilize on-board cameras in an end-to-end differentiable model for metric self-localization on unseen orthophotos.
arXiv Detail & Related papers (2022-03-07T12:25:44Z)
Visual and Object Geo-localization: A Comprehensive Survey [11.120155713865918]
Geo-localization refers to the process of determining where on earth some entity' is located. This paper provides a comprehensive survey of geo-localization involving images, which involves either determining from where an image has been captured (Image geo-localization) or geo-locating objects within an image (Object geo-localization) We will provide an in-depth study, including a summary of popular algorithms, a description of proposed datasets, and an analysis of performance results to illustrate the current state of each field.
arXiv Detail & Related papers (2021-12-30T20:46:53Z)
Semantic Image Alignment for Vehicle Localization [111.59616433224662]
We present a novel approach to vehicle localization in dense semantic maps using semantic segmentation from a monocular camera. In contrast to existing visual localization approaches, the system does not require additional keypoint features, handcrafted localization landmark extractors or expensive LiDAR sensors.
arXiv Detail & Related papers (2021-10-08T14:40:15Z)
GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments [54.21959527308051]
We present a new learning-based method for identifying safe and navigable regions in off-road terrains and unstructured environments from RGB images. Our approach consists of classifying groups of terrain classes based on their navigability levels using coarse-grained semantic segmentation. We show through extensive evaluations on the RUGD and RELLIS-3D datasets that our learning algorithm improves the accuracy of visual perception in off-road terrains for navigation.
arXiv Detail & Related papers (2021-03-07T02:16:24Z)
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization [54.00111565818903]
Cross-view geo-localization is to spot images of the same geographic target from different platforms. Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center. We introduce a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information.
arXiv Detail & Related papers (2020-08-26T16:06:11Z)

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