# Satellite Image-based Localization via Learned Embeddings

arXiv: https://arxiv.org/abs/1704.01133 · HTML: https://mlsanity.com/paper/1704.01133/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2017.

## Built on by (most cited first, 22 of 22)

- [Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence](https://mlsanity.com/paper/2212.04074/) (2022)
- [Cross-View Image Sequence Geo-localization](https://mlsanity.com/paper/2210.14295/) (2022)
- [GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement](https://mlsanity.com/paper/2308.09624/) (2023)
- [GAMa: Cross-view Video Geo-localization](https://mlsanity.com/paper/2207.02431/) (2022)
- [Visual and Object Geo-localization: A Comprehensive Survey](https://mlsanity.com/paper/2112.15202/) (2021)
- [City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent](https://mlsanity.com/paper/2203.05612/) (2022)
- [Cross-View Image Set Geo-Localization](https://mlsanity.com/paper/2412.18852/) (2024)
- [Wide-Area Geolocalization with a Limited Field of View Camera](https://mlsanity.com/paper/2209.11854/) (2022)
- [CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data](https://mlsanity.com/paper/2204.07052/) (2022)
- [A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios](https://mlsanity.com/paper/2505.07622/) (2025)
- [Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs](https://mlsanity.com/paper/2303.02076/) (2023)
- [Energy-Based Models for Cross-Modal Localization using Convolutional Transformers](https://mlsanity.com/paper/2306.04021/) (2023)
- [LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2403.06813/) (2024)
- [MRGeo: Robust Cross-View Geo-Localization of Corrupted Images via Spatial and Channel Feature Enhancement](https://mlsanity.com/paper/2603.12587/) (2026)
- [View-Invariant Localization using Semantic Objects in Changing Environments](https://mlsanity.com/paper/2209.14426/) (2022)
- [Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination Methods](https://mlsanity.com/paper/2306.16122/) (2023)
- [Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings](https://mlsanity.com/paper/2412.11529/) (2024)
- [A Survey on Visual Map Localization Using LiDARs and Cameras](https://mlsanity.com/paper/2208.03376/) (2022)
- [BEVMAPMATCH: Multimodal BEV Neural Map Matching for Robust Re-Localization of Autonomous Vehicles](https://mlsanity.com/paper/2603.25963/) (2026)
- [Damage Estimation and Localization from Sparse Aerial Imagery](https://mlsanity.com/paper/2111.03708/) (2021)
- [Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what](https://mlsanity.com/paper/2405.09194/) (2024)
- [Learning Holistic Scene Layout against View Shifts in Cross-View Geo-Localization](https://mlsanity.com/paper/2610.13553/) (2026)

## Abstract (arXiv)

We propose a vision-based method that localizes a ground vehicle using publicly available satellite imagery as the only prior knowledge of the environment. Our approach takes as input a sequence of ground-level images acquired by the vehicle as it navigates, and outputs an estimate of the vehicle's pose relative to a georeferenced satellite image. We overcome the significant viewpoint and appearance variations between the images through a neural multi-view model that learns location-discriminative embeddings in which ground-level images are matched with their corresponding satellite view of the scene. We use this learned function as an observation model in a filtering framework to maintain a distribution over the vehicle's pose. We evaluate our method on different benchmark datasets and demonstrate its ability localize ground-level images in environments novel relative to training, despite the challenges of significant viewpoint and appearance variations.