# Remote Awareness of Seafloor Images Collected by AUVs over Low-Bandwidth Communication Links

arXiv: https://arxiv.org/abs/2607.18013 · HTML: https://mlsanity.com/paper/2607.18013/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery](https://mlsanity.com/paper/2509.06660/) (2025)
- [Autonomous Subsea Cable Search and Tracking with Graph-Optimised Priors and Visual Tracking](https://mlsanity.com/paper/2606.23606/) (2026)

## Abstract (arXiv)

This paper introduces a method for real-time processing and transmission of autonomous underwater vehicle (AUV) imagery over low-bandwidth communication links. It leverages artificial intelligence (AI) techniques to identify a set of images that best represent an entire dataset, or automatically finds the most similar images to a given query image for transmission to operators. Combined with metadata of a larger set of images, compressed versions of the selected images can be transmitted over satellite communication links or underwater modems, and provide operators on shore with information about the type of imagery the AUV is collecting while it is still deployed. Data from three deployments off the coast of the UK and in Gran Canaria using different AUVs and imaging systems demonstrate the method in the field. It achieved an almost 400,000-fold reduction in data volume compared to the raw data size, enabling transmission of data summaries of a 2-hour 47-minute-long mapping mission in just over 34 minutes over low-bandwidth satellite communication.