# View Distribution Alignment with Progressive Adversarial Learning for UAV Visual Geo-Localization

arXiv: https://arxiv.org/abs/2401.01573 · HTML: https://mlsanity.com/paper/2401.01573/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 3 Jan 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization](https://mlsanity.com/paper/2002.12186/) (2020)
- [Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization](https://mlsanity.com/paper/2008.11646/) (2020)
- [Bridging the Domain Gap for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/1904.11045/) (2019)
- [Predicting Ground-Level Scene Layout from Aerial Imagery](https://mlsanity.com/paper/1612.02709/) (2016)
- [Cross-View Image Matching for Geo-localization in Urban Environments](https://mlsanity.com/paper/1703.07815/) (2017)
- [A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization](https://mlsanity.com/paper/2201.09206/) (2022)
- [Object Tracking and Geo-localization from Street Images](https://mlsanity.com/paper/2107.06257/) (2021)

## Built on by (most cited first, 1 of 1)

- [Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone](https://mlsanity.com/paper/2512.02737/) (2025)

## Abstract (arXiv)

Unmanned Aerial Vehicle (UAV) visual geo-localization aims to match images of the same geographic target captured from different views, i.e., the UAV view and the satellite view. It is very challenging due to the large appearance differences in UAV-satellite image pairs. Previous works map images captured by UAVs and satellites to a shared feature space and employ a classification framework to learn location-dependent features while neglecting the overall distribution shift between the UAV view and the satellite view. In this paper, we address these limitations by introducing distribution alignment of the two views to shorten their distance in a common space. Specifically, we propose an end-to-end network, called PVDA (Progressive View Distribution Alignment). During training, feature encoder, location classifier, and view discriminator are jointly optimized by a novel progressive adversarial learning strategy. Competition between feature encoder and view discriminator prompts both of them to be stronger. It turns out that the adversarial learning is progressively emphasized until UAV-view images are indistinguishable from satellite-view images. As a result, the proposed PVDA becomes powerful in learning location-dependent yet view-invariant features with good scalability towards unseen images of new locations. Compared to the state-of-the-art methods, the proposed PVDA requires less inference time but has achieved superior performance on the University-1652 dataset.