# Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation

arXiv: https://arxiv.org/abs/2005.11592 · HTML: https://mlsanity.com/paper/2005.11592/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2020.

## Built on by (most cited first, 30 of 36)

- [TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization](https://mlsanity.com/paper/2204.00097/) (2022)
- [Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image](https://mlsanity.com/paper/2204.04752/) (2022)
- [Visual Cross-View Metric Localization with Dense Uncertainty Estimates](https://mlsanity.com/paper/2208.08519/) (2022)
- [SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation](https://mlsanity.com/paper/2211.14651/) (2022)
- [Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer](https://mlsanity.com/paper/2307.08015/) (2023)
- [Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching](https://mlsanity.com/paper/2203.14148/) (2022)
- [Convolutional Cross-View Pose Estimation](https://mlsanity.com/paper/2303.05915/) (2023)
- [Cross-View Image Sequence Geo-localization](https://mlsanity.com/paper/2210.14295/) (2022)
- [Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization](https://mlsanity.com/paper/2403.14198/) (2024)
- [GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement](https://mlsanity.com/paper/2308.09624/) (2023)
- [ConGeo: Robust Cross-view Geo-localization across Ground View Variations](https://mlsanity.com/paper/2403.13965/) (2024)
- [CVLNet: Cross-View Semantic Correspondence Learning for Video-based Camera Localization](https://mlsanity.com/paper/2208.03660/) (2022)
- [CV-Cities: Advancing Cross-View Geo-Localization in Global Cities](https://mlsanity.com/paper/2411.12431/) (2024)
- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation](https://mlsanity.com/paper/2105.07645/) (2021)
- [Satellite Image Based Cross-view Localization for Autonomous Vehicle](https://mlsanity.com/paper/2207.13506/) (2022)
- [Cross-view geo-localization: a survey](https://mlsanity.com/paper/2406.09722/) (2024)
- [Weakly-supervised Camera Localization by Ground-to-satellite Image Registration](https://mlsanity.com/paper/2409.06471/) (2024)
- [Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials](https://mlsanity.com/paper/2307.03398/) (2023)
- [Visual and Object Geo-localization: A Comprehensive Survey](https://mlsanity.com/paper/2112.15202/) (2021)
- [City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent](https://mlsanity.com/paper/2203.05612/) (2022)
- [C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation](https://mlsanity.com/paper/2312.08060/) (2023)
- [GAReT: Cross-view Video Geolocalization with Adapters and Auto-Regressive Transformers](https://mlsanity.com/paper/2408.02840/) (2024)
- [BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation](https://mlsanity.com/paper/2312.15363/) (2023)
- [Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection](https://mlsanity.com/paper/2306.17431/) (2023)
- [Orientation-Guided Contrastive Learning for UAV-View Geo-Localisation](https://mlsanity.com/paper/2308.00982/) (2023)
- [Wide-Area Geolocalization with a Limited Field of View Camera](https://mlsanity.com/paper/2209.11854/) (2022)
- [SpaGBOL: Spatial-Graph-Based Orientated Localisation](https://mlsanity.com/paper/2409.15514/) (2024)
- [Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis](https://mlsanity.com/paper/2412.03315/) (2024)
- [A Survey on Visual Map Localization Using LiDARs and Cameras](https://mlsanity.com/paper/2208.03376/) (2022)

## Abstract (arXiv)

Street-to-aerial image geo-localization, which matches a query street-view image to the GPS-tagged aerial images in a reference set, has attracted increasing attention recently. In this paper, we revisit this problem and point out the ignored issue about image alignment information. We show that the performance of a simple Siamese network is highly dependent on the alignment setting and the comparison of previous works can be unfair if they have different assumptions. Instead of focusing on the feature extraction under the alignment assumption, we show that improvements in metric learning techniques significantly boost the performance regardless of the alignment. Without leveraging the alignment information, our pipeline outperforms previous works on both panorama and cropped datasets. Furthermore, we conduct visualization to help understand the learned model and the effect of alignment information using Grad-CAM. With our discovery on the approximate rotation-invariant activation maps, we propose a novel method to estimate the orientation/alignment between a pair of cross-view images with unknown alignment information. It achieves state-of-the-art results on the CVUSA dataset.