# VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments

arXiv: https://arxiv.org/abs/2205.11567 · HTML: https://mlsanity.com/paper/2205.11567/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 7 of 7)

- [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)
- [VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change](https://mlsanity.com/paper/2005.08135/) (2020)
- [Single-View Place Recognition under Seasonal Changes](https://mlsanity.com/paper/1808.06516/) (2018)
- [RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching](https://mlsanity.com/paper/2103.08573/) (2021)
- [Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization](https://mlsanity.com/paper/2202.01821/) (2022)
- [Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?](https://mlsanity.com/paper/1904.07967/) (2019)

## Built on by (most cited first, 15 of 15)

- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [Revisit Anything: Visual Place Recognition via Image Segment Retrieval](https://mlsanity.com/paper/2409.18049/) (2024)
- [OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata](https://mlsanity.com/paper/2509.18350/) (2025)
- [Visual place recognition for aerial imagery: A survey](https://mlsanity.com/paper/2406.00885/) (2024)
- [Survey on Datasets for Perception in Unstructured Outdoor Environments](https://mlsanity.com/paper/2404.18750/) (2024)
- [Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments](https://mlsanity.com/paper/2503.04096/) (2025)
- [Faster or Stronger: Towards Flexible Visual Place Recognition via Weighted Aggregation and Token Pruning](https://mlsanity.com/paper/2605.20551/) (2026)
- [MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity](https://mlsanity.com/paper/2505.17591/) (2025)
- [Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation](https://mlsanity.com/paper/2603.22153/) (2026)
- [Towards Lifelong Aerial Autonomy: Geometric Memory Management for Continual Visual Place Recognition in Dynamic Environments](https://mlsanity.com/paper/2604.09038/) (2026)
- [OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos](https://mlsanity.com/paper/2606.25245/) (2026)
- [VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition](https://mlsanity.com/paper/2403.09025/) (2024)
- [Visual Place Recognition for Large-Scale UAV Applications](https://mlsanity.com/paper/2507.15089/) (2025)
- [Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone](https://mlsanity.com/paper/2512.02737/) (2025)
- [Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2609.25937/) (2026)

## Abstract (arXiv)

Visual Place Recognition and Visual Localization are essential components in navigation and mapping for autonomous vehicles especially in GNSS-denied navigation scenarios. Recent work has focused on ground or close to ground applications such as self-driving cars or indoor-scenarios and low-altitude drone flights. However, applications such as Urban Air Mobility require operations in large-scale outdoor environments at medium to high altitudes. We present a new dataset named VPAIR. The dataset was recorded on board a light aircraft flying at an altitude of more than 300 meters above ground capturing images with a downwardfacing camera. Each image is paired with a high resolution reference render including dense depth information and 6-DoF reference poses. The dataset covers a more than one hundred kilometers long trajectory over various types of challenging landscapes, e.g. urban, farmland and forests. Experiments on this dataset illustrate the challenges introduced by the change in perspective to a bird's eye view such as in-plane rotations.