# Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles

arXiv: https://arxiv.org/abs/2108.03344 · HTML: https://mlsanity.com/paper/2108.03344/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 7 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Cross-View Image Matching for Geo-localization in Urban Environments](https://mlsanity.com/paper/1703.07815/) (2017)
- [Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization](https://mlsanity.com/paper/1809.01019/) (2018)
- [Image-Based Geo-Localization Using Satellite Imagery](https://mlsanity.com/paper/1903.00159/) (2019)
- [UAV Pose Estimation using Cross-view Geolocalization with Satellite Imagery](https://mlsanity.com/paper/1809.05979/) (2018)
- [UAV Localization Using Autoencoded Satellite Images](https://mlsanity.com/paper/2102.05692/) (2021)
- [Deep UAV Localization with Reference View Rendering](https://mlsanity.com/paper/2008.04619/) (2020)

## Built on by (most cited first, 10 of 10)

- [UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization](https://mlsanity.com/paper/2401.05971/) (2024)
- [LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment](https://mlsanity.com/paper/2410.12269/) (2024)
- [Long-range UAV Thermal Geo-localization with Satellite Imagery](https://mlsanity.com/paper/2306.02994/) (2023)
- [OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata](https://mlsanity.com/paper/2509.18350/) (2025)
- [LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment](https://mlsanity.com/paper/2507.00659/) (2025)
- [Visual place recognition for aerial imagery: A survey](https://mlsanity.com/paper/2406.00885/) (2024)
- [Learning autonomous driving from aerial imagery](https://mlsanity.com/paper/2410.14177/) (2024)
- [LoD-Loc v3: Generalized Aerial Localization in Dense Cities using Instance Silhouette Alignment](https://mlsanity.com/paper/2603.19609/) (2026)
- [PiLoT v2: Pixel-to-Orthogonal Map Alignment for Free-view UAV Geo-localization](https://mlsanity.com/paper/2606.31098/) (2026)
- [DECO: Depth-Guided Co-Visibility Reasoning for Low-Altitude UAV Visual Localization](https://mlsanity.com/paper/2608.22289/) (2026)

## Abstract (arXiv)

The capabilities of autonomous flight with unmanned aerial vehicles (UAVs) have significantly increased in recent times. However, basic problems such as fast and robust geo-localization in GPS-denied environments still remain unsolved. Existing research has primarily concentrated on improving the accuracy of localization at the cost of long and varying computation time in various situations, which often necessitates the use of powerful ground station machines. In order to make image-based geo-localization online and pragmatic for lightweight embedded systems on UAVs, we propose a framework that is reliable in changing scenes, flexible about computing resource allocation and adaptable to common camera placements. The framework is comprised of two stages: offline database preparation and online inference. At the first stage, color images and depth maps are rendered as seen from potential vehicle poses quantized over the satellite and topography maps of anticipated flying areas. A database is then populated with the global and local descriptors of the rendered images. At the second stage, for each captured real-world query image, top global matches are retrieved from the database and the vehicle pose is further refined via local descriptor matching. We present field experiments of image-based localization on two different UAV platforms to validate our results.