# BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation

arXiv: https://arxiv.org/abs/2312.15363 · HTML: https://mlsanity.com/paper/2312.15363/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 25 of 25)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Wide-Area Image Geolocalization with Aerial Reference Imagery](https://mlsanity.com/paper/1510.03743/) (2015)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization](https://mlsanity.com/paper/2204.00097/) (2022)
- [Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching](https://mlsanity.com/paper/2005.03860/) (2020)
- [Translating Images into Maps](https://mlsanity.com/paper/2110.00966/) (2021)
- [Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization](https://mlsanity.com/paper/2103.06818/) (2021)
- [Bridging the Domain Gap for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/1904.11045/) (2019)
- [Localizing and Orienting Street Views Using Overhead Imagery](https://mlsanity.com/paper/1608.00161/) (2016)
- [Optimal Feature Transport for Cross-View Image Geo-Localization](https://mlsanity.com/paper/1907.05021/) (2019)
- [Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image](https://mlsanity.com/paper/2204.04752/) (2022)
- [Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks](https://mlsanity.com/paper/1804.02176/) (2018)
- [Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence](https://mlsanity.com/paper/2212.04074/) (2022)
- [Uncertainty-aware Vision-based Metric Cross-view Geolocalization](https://mlsanity.com/paper/2211.12145/) (2022)
- [Learning to Look around Objects for Top-View Representations of Outdoor Scenes](https://mlsanity.com/paper/1803.10870/) (2018)
- [Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation](https://mlsanity.com/paper/2005.11592/) (2020)
- [CVLNet: Cross-View Semantic Correspondence Learning for Video-based Camera Localization](https://mlsanity.com/paper/2208.03660/) (2022)
- [GeoCapsNet: Aerial to Ground view Image Geo-localization using Capsule Network](https://mlsanity.com/paper/1904.06281/) (2019)
- ["The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping](https://mlsanity.com/paper/2204.02944/) (2022)

## Built on by (most cited first, 9 of 9)

- [SpaGBOL: Spatial-Graph-Based Orientated Localisation](https://mlsanity.com/paper/2409.15514/) (2024)
- [PEnG: Pose-Enhanced Geo-Localisation](https://mlsanity.com/paper/2411.15742/) (2024)
- [From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization](https://mlsanity.com/paper/2503.07520/) (2025)
- [SinGeo: Unlock Single Model's Potential for Robust Cross-View Geo-Localization](https://mlsanity.com/paper/2603.09377/) (2026)
- [RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization](https://mlsanity.com/paper/2603.27758/) (2026)
- [VVLoc: Prior-free 3-DoF Vehicle Visual Localization](https://mlsanity.com/paper/2602.00810/) (2026)
- [GeoLink: A 3D-aware Framework to Improve Generalization for Cross-view Geo-localization](https://mlsanity.com/paper/2604.13183/) (2026)
- [TACO: Trajectory Aligning Cross-view Optimisation](https://mlsanity.com/paper/2605.03315/) (2026)
- [Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving](https://mlsanity.com/paper/2608.10660/) (2026)

## Abstract (arXiv)

Cross-view image matching for geo-localisation is a challenging problem due to the significant visual difference between aerial and ground-level viewpoints. The method provides localisation capabilities from geo-referenced images, eliminating the need for external devices or costly equipment. This enhances the capacity of agents to autonomously determine their position, navigate, and operate effectively in GNSS-denied environments. Current research employs a variety of techniques to reduce the domain gap such as applying polar transforms to aerial images or synthesising between perspectives. However, these approaches generally rely on having a 360deg field of view, limiting real-world feasibility. We propose BEV-CV, an approach introducing two key novelties with a focus on improving the real-world viability of cross-view geo-localisation. Firstly bringing ground-level images into a semantic Birds-Eye-View before matching embeddings, allowing for direct comparison with aerial image representations. Secondly, we adapt datasets into application realistic format - limited Field-of-View images aligned to vehicle direction. BEV-CV achieves state-of-the-art recall accuracies, improving Top-1 rates of 70deg crops of CVUSA and CVACT by 23% and 24% respectively. Also decreasing computational requirements by reducing floating point operations to below previous works, and decreasing embedding dimensionality by 33% - together allowing for faster localisation capabilities.