# Cross-View Image Sequence Geo-localization

arXiv: https://arxiv.org/abs/2210.14295 · HTML: https://mlsanity.com/paper/2210.14295/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [End-to-End Video Instance Segmentation with Transformers](https://mlsanity.com/paper/2011.14503/) (2020)
- [VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval](https://mlsanity.com/paper/2011.12172/) (2020)
- [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)
- [Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching](https://mlsanity.com/paper/2005.03860/) (2020)
- [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)
- [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)
- [Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation](https://mlsanity.com/paper/2005.11592/) (2020)
- [Semantic Cross-View Matching](https://mlsanity.com/paper/1511.00098/) (2015)
- [Satellite Image-based Localization via Learned Embeddings](https://mlsanity.com/paper/1704.01133/) (2017)
- [UAV Pose Estimation using Cross-view Geolocalization with Satellite Imagery](https://mlsanity.com/paper/1809.05979/) (2018)
- [Visual and Object Geo-localization: A Comprehensive Survey](https://mlsanity.com/paper/2112.15202/) (2021)

## Built on by (most cited first, 30 of 31)

- [GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement](https://mlsanity.com/paper/2308.09624/) (2023)
- [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)
- [Statewide Visual Geolocalization in the Wild](https://mlsanity.com/paper/2409.16763/) (2024)
- [Towards Interactive Global Geolocation Assistant](https://mlsanity.com/paper/2412.08907/) (2024)
- [GAReT: Cross-view Video Geolocalization with Adapters and Auto-Regressive Transformers](https://mlsanity.com/paper/2408.02840/) (2024)
- [CityGuessr: City-Level Video Geo-Localization on a Global Scale](https://mlsanity.com/paper/2411.06344/) (2024)
- [Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance](https://mlsanity.com/paper/2408.04224/) (2024)
- [Cross-View Image Set Geo-Localization](https://mlsanity.com/paper/2412.18852/) (2024)
- [Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis](https://mlsanity.com/paper/2412.03315/) (2024)
- [A Semantic Segmentation-guided Approach for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/2404.11302/) (2024)
- [VICI: VLM-Instructed Cross-view Image-localisation](https://mlsanity.com/paper/2507.04107/) (2025)
- [CVD-SfM: A Cross-View Deep Front-end Structure-from-Motion System for Sparse Localization in Multi-Altitude Scenes](https://mlsanity.com/paper/2508.01936/) (2025)
- [Temporal Attention for Cross-View Sequential Image Localization](https://mlsanity.com/paper/2408.15569/) (2024)
- [RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization](https://mlsanity.com/paper/2603.27758/) (2026)
- [Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization](https://mlsanity.com/paper/2604.10721/) (2026)
- [SeqLoc: Beyond the Single Frame for Cross-View Geo-Localization in Feature-Sparse Scenes](https://mlsanity.com/paper/2608.07835/) (2026)
- [AeroGround: A Comprehensive Benchmark for Aerial-Ground Collaborative Reasoning](https://mlsanity.com/paper/2608.14721/) (2026)
- [Visual Geo-Localization from images](https://mlsanity.com/paper/2407.14910/) (2024)
- [STRMs: Spatial Temporal Reasoning Models for Vision-Based Localization Rivaling GPS Precision](https://mlsanity.com/paper/2503.07939/) (2025)
- [Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone](https://mlsanity.com/paper/2512.02737/) (2025)
- [Wrivinder: Towards Spatial Intelligence for Geo-locating Ground Images onto Satellite Imagery](https://mlsanity.com/paper/2602.14929/) (2026)
- [Where Do Vision-Language Models Fail? World Scale Analysis for Image Geolocalization](https://mlsanity.com/paper/2604.16248/) (2026)
- [Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse](https://mlsanity.com/paper/2605.14925/) (2026)
- [STEAM: Stable Self-Training with Elastic Matching and Adaptive Purification](https://mlsanity.com/paper/2607.09057/) (2026)
- [Trajectory-aware Cross-view Geo-localization with Sequential Observations](https://mlsanity.com/paper/2607.15491/) (2026)
- [Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving](https://mlsanity.com/paper/2608.10660/) (2026)
- [GeoBridge: Decoupled Semantic Conditioning for Generative Image Geolocalization](https://mlsanity.com/paper/2608.11838/) (2026)
- [X2Localizer: Cross-grained Alignment for Progressive Cross-view Video Geo-localization](https://mlsanity.com/paper/2608.16658/) (2026)
- [Ground-to-Satellite Localization in Unconstrained Image Collections for 3D Scene Reconstruction](https://mlsanity.com/paper/2608.29211/) (2026)

## Abstract (arXiv)

Cross-view geo-localization aims to estimate the GPS location of a query ground-view image by matching it to images from a reference database of geo-tagged aerial images. To address this challenging problem, recent approaches use panoramic ground-view images to increase the range of visibility. Although appealing, panoramic images are not readily available compared to the videos of limited Field-Of-View (FOV) images. In this paper, we present the first cross-view geo-localization method that works on a sequence of limited FOV images. Our model is trained end-to-end to capture the temporal structure that lies within the frames using the attention-based temporal feature aggregation module. To robustly tackle different sequences length and GPS noises during inference, we propose to use a sequential dropout scheme to simulate variant length sequences. To evaluate the proposed approach in realistic settings, we present a new large-scale dataset containing ground-view sequences along with the corresponding aerial-view images. Extensive experiments and comparisons demonstrate the superiority of the proposed approach compared to several competitive baselines.