# Satellite-Free Training for Drone-View Geo-Localization

arXiv: https://arxiv.org/abs/2604.01581 · HTML: https://mlsanity.com/paper/2604.01581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 17 of 17)

- [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)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [MambaVision: A Hybrid Mamba-Transformer Vision Backbone](https://mlsanity.com/paper/2407.08083/) (2024)
- [University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization](https://mlsanity.com/paper/2002.12186/) (2020)
- [Dual-Path Convolutional Image-Text Embeddings with Instance Loss](https://mlsanity.com/paper/1711.05535/) (2017)
- [LENS: Localization enhanced by NeRF synthesis](https://mlsanity.com/paper/2110.06558/) (2021)
- [Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation](https://mlsanity.com/paper/2303.11851/) (2023)
- [Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization](https://mlsanity.com/paper/2008.11646/) (2020)
- [A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization](https://mlsanity.com/paper/2201.09206/) (2022)
- [Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization](https://mlsanity.com/paper/2403.14198/) (2024)
- [Learning Cross-view Visual Geo-localization without Ground Truth](https://mlsanity.com/paper/2403.12702/) (2024)
- [Unsupervised Multi-view UAV Image Geo-localization via Iterative Rendering](https://mlsanity.com/paper/2411.14816/) (2024)
- [WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization](https://mlsanity.com/paper/2508.09560/) (2025)
- [Without Paired Labeled Data: End-to-End Self-Supervised Learning for Drone-view Geo-Localization](https://mlsanity.com/paper/2502.11381/) (2025)
- [DiffusionUavLoc: Visually Prompted Diffusion for Cross-View UAV Localization](https://mlsanity.com/paper/2511.06422/) (2025)

## Abstract (arXiv)

Drone-view geo-localization (DVGL) typically assumes access to satellite imagery during task-specific development. We study a late-arriving-gallery setting, where model development relies only on grouped source UAV observations and the target satellite gallery remains unavailable until the model is frozen. We propose ours, Orbit Completion Learning, which uses source-side multi-view geometry as privileged training information while preserving image-only deployment. OCL reconstructs each source scene using structure-from-motion and 3D Gaussian Splatting, and renders canonical complementary views to complete its observation orbit. An exponential-moving-average teacher aggregates the completed orbit into a scene-level target, while a variable-cardinality student learns to recover this target from partial observations through distillation and contrastive learning. At inference, the frozen student independently encodes UAV queries and satellite gallery images using the same single-image path, without reconstruction or target-gallery adaptation. Experiments on University-1652, SUES-200, and IRVL328, together with nine weather-style appearance shifts, evaluate generalization across altitude, modality, and appearance variations. OCL achieves 57.85% R@1 and 68.44% mAP on University-1652, and obtains the best R@1 among the evaluated target-satellite-free methods in eight of nine weather conditions. Controlled ablations further show that completed teacher context provides gains beyond student-side synthetic-view augmentation. The code and data are publicly available at https://github.com/liutao23/OCL-DVGL.