# UAV Pose Estimation using Cross-view Geolocalization with Satellite Imagery

arXiv: https://arxiv.org/abs/1809.05979 · HTML: https://mlsanity.com/paper/1809.05979/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2018.

## Built on by (most cited first, 21 of 21)

- [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)
- [UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization](https://mlsanity.com/paper/2401.05971/) (2024)
- [Cross-view geo-localization: a survey](https://mlsanity.com/paper/2406.09722/) (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)
- [Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2108.03344/) (2021)
- [STHN: Deep Homography Estimation for UAV Thermal Geo-localization with Satellite Imagery](https://mlsanity.com/paper/2405.20470/) (2024)
- [Visual place recognition for aerial imagery: A survey](https://mlsanity.com/paper/2406.00885/) (2024)
- [A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios](https://mlsanity.com/paper/2505.07622/) (2025)
- [Generalized Maximum Likelihood Estimation for Perspective-n-Point Problem](https://mlsanity.com/paper/2408.01945/) (2024)
- [Object-level Cross-view Geo-localization with Location Enhancement and Multi-Head Cross Attention](https://mlsanity.com/paper/2505.17911/) (2025)
- [Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation](https://mlsanity.com/paper/2603.22153/) (2026)
- [Unifying UAV Cross-View Geo-Localization via 3D Geometric Perception](https://mlsanity.com/paper/2604.01747/) (2026)
- [Damage Estimation and Localization from Sparse Aerial Imagery](https://mlsanity.com/paper/2111.03708/) (2021)
- [Cross-view Localization and Synthesis -- Datasets, Challenges and Opportunities](https://mlsanity.com/paper/2510.22736/) (2025)
- [Where Do Vision-Language Models Fail? World Scale Analysis for Image Geolocalization](https://mlsanity.com/paper/2604.16248/) (2026)
- [TACO: Trajectory Aligning Cross-view Optimisation](https://mlsanity.com/paper/2605.03315/) (2026)
- [STEAM: Stable Self-Training with Elastic Matching and Adaptive Purification](https://mlsanity.com/paper/2607.09057/) (2026)
- [Structure-Aware Matching for Robust UAV Thermal-to-Satellite Geo-Localization](https://mlsanity.com/paper/2610.12835/) (2026)

## Abstract (arXiv)

We propose an image-based cross-view geolocalization method that estimates the global pose of a UAV with the aid of georeferenced satellite imagery. Our method consists of two Siamese neural networks that extract relevant features despite large differences in viewpoints. The input to our method is an aerial UAV image and nearby satellite images, and the output is the weighted global pose estimate of the UAV camera. We also present a framework to integrate our cross-view geolocalization output with visual odometry through a Kalman filter. We build a dataset of simulated UAV images and satellite imagery to train and test our networks. We show that our method performs better than previous camera pose estimation methods, and we demonstrate our networks ability to generalize well to test datasets with unseen images. Finally, we show that integrating our method with visual odometry significantly reduces trajectory estimation errors.