# You Are Here: Geolocation by Embedding Maps and Images

arXiv: https://arxiv.org/abs/1911.08797 · HTML: https://mlsanity.com/paper/1911.08797/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 19 of 19)

- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [F3Loc: Fusion and Filtering for Floorplan Localization](https://mlsanity.com/paper/2403.03370/) (2024)
- [MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps](https://mlsanity.com/paper/2407.08561/) (2024)
- [Cross-view geo-localization: a survey](https://mlsanity.com/paper/2406.09722/) (2024)
- [From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models](https://mlsanity.com/paper/2508.01608/) (2025)
- [SegLocNet: Multimodal Localization Network for Autonomous Driving via Bird's-Eye-View Segmentation](https://mlsanity.com/paper/2502.20077/) (2025)
- [U-ViLAR: Uncertainty-Aware Visual Localization for Autonomous Driving via Differentiable Association and Registration](https://mlsanity.com/paper/2507.04503/) (2025)
- [Image-based Geolocalization by Ground-to-2.5D Map Matching](https://mlsanity.com/paper/2308.05993/) (2023)
- [Learning to be Smooth: An End-to-End Differentiable Particle Smoother](https://mlsanity.com/paper/2502.10546/) (2025)
- [DeepSSN: a deep convolutional neural network to assess spatial scene similarity](https://mlsanity.com/paper/2202.04755/) (2022)
- [Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images](https://mlsanity.com/paper/2411.12620/) (2024)
- [Evaluating Global Geo-alignment for Precision Learned Autonomous Vehicle Localization using Aerial Data](https://mlsanity.com/paper/2503.13896/) (2025)
- [3DoF Localization from a Single Image and an Object Map: the Flatlandia Problem and Dataset](https://mlsanity.com/paper/2304.06373/) (2023)
- [BEV-Patch-PF: Particle Filtering with BEV-Aerial Feature Matching for Off-Road Geo-Localization](https://mlsanity.com/paper/2512.15111/) (2025)
- [HOLO: Homography-Guided Pose Estimator Network for Fine-Grained Visual Localization on SD Maps](https://mlsanity.com/paper/2601.02730/) (2026)
- [Uncertainty-Aware Hierarchical Re-Localization in OpenStreetMap via Semantic Alignment](https://mlsanity.com/paper/2603.01613/) (2026)
- [TOL: Textual Localization with OpenStreetMap](https://mlsanity.com/paper/2604.01644/) (2026)
- [MAG-VLAQ: Multi-modal Aerial-Ground Query Aggregation for Cross-View Place Recognition](https://mlsanity.com/paper/2605.09418/) (2026)
- [Leveraging Semantic Maps for City-Scale Cross-View Localization](https://mlsanity.com/paper/2607.25215/) (2026)

## Abstract (arXiv)

We present a novel approach to geolocalising panoramic images on a 2-D cartographic map based on learning a low dimensional embedded space, which allows a comparison between an image captured at a location and local neighbourhoods of the map. The representation is not sufficiently discriminatory to allow localisation from a single image, but when concatenated along a route, localisation converges quickly, with over 90% accuracy being achieved for routes of around 200m in length when using Google Street View and Open Street Map data. The method generalises a previous fixed semantic feature based approach and achieves significantly higher localisation accuracy and faster convergence.