# AstroLoc: Robust Space to Ground Image Localizer

arXiv: https://arxiv.org/abs/2502.07003 · HTML: https://mlsanity.com/paper/2502.07003/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 21 of 21)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [Geography-Aware Self-Supervised Learning](https://mlsanity.com/paper/2011.09980/) (2020)
- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [Optimal Transport Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2311.15937/) (2023)
- [Self-supervising Fine-grained Region Similarities for Large-scale Image Localization](https://mlsanity.com/paper/2006.03926/) (2020)
- [GSV-Cities: Toward Appropriate Supervised Visual Place Recognition](https://mlsanity.com/paper/2210.10239/) (2022)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning](https://mlsanity.com/paper/2008.00975/) (2020)
- [The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement](https://mlsanity.com/paper/2404.10438/) (2024)
- [UAV Localization Using Autoencoded Satellite Images](https://mlsanity.com/paper/2102.05692/) (2021)
- [EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography](https://mlsanity.com/paper/2405.05422/) (2024)
- [Global Proxy-based Hard Mining for Visual Place Recognition](https://mlsanity.com/paper/2302.14217/) (2023)
- [FoundLoc: Vision-based Onboard Aerial Localization in the Wild](https://mlsanity.com/paper/2310.16299/) (2023)
- [Orientation-Guided Contrastive Learning for UAV-View Geo-Localisation](https://mlsanity.com/paper/2308.00982/) (2023)
- [EarthLoc: Astronaut Photography Localization by Indexing Earth from Space](https://mlsanity.com/paper/2403.06758/) (2024)
- [A Transformer-Based Adaptive Semantic Aggregation Method for UAV Visual Geo-Localization](https://mlsanity.com/paper/2401.01574/) (2024)

## Built on by (most cited first, 1 of 1)

- [ELViS: Efficient Visual Similarity from Local Descriptors that Generalizes Across Domains](https://mlsanity.com/paper/2603.28603/) (2026)

## Abstract (arXiv)

Astronauts take thousands of photos of Earth per day from the International Space Station, which, once localized on Earth's surface, are used for a multitude of tasks, ranging from climate change research to disaster management. The localization process, which has been performed manually for decades, has recently been approached through image retrieval solutions: given an astronaut photo, find its most similar match among a large database of geo-tagged satellite images, in a task called Astronaut Photography Localization (APL). Yet, existing APL approaches are trained only using satellite images, without taking advantage of the millions open-source astronaut photos. In this work we present the first APL pipeline capable of leveraging astronaut photos for training. We first produce full localization information for 300,000 manually weakly labeled astronaut photos through an automated pipeline, and then use these images to train a model, called AstroLoc. AstroLoc learns a robust representation of Earth's surface features through two losses: astronaut photos paired with their matching satellite counterparts in a pairwise loss, and a second loss on clusters of satellite imagery weighted by their relevance to astronaut photography via unsupervised mining. We find that AstroLoc achieves a staggering 35% average improvement in recall@1 over previous SOTA, pushing the limits of existing datasets with a recall@100 consistently over 99%. Finally, we note that AstroLoc, without any fine-tuning, provides excellent results for related tasks like the lost-in-space satellite problem and historical space imagery localization.