# AI-Driven Marine Robotics: Emerging Trends in Underwater Perception and Ecosystem Monitoring

arXiv: https://arxiv.org/abs/2509.01878 · HTML: https://mlsanity.com/paper/2509.01878/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 68 references that are in the map.
- First posted on arXiv 2 Sep 2025.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [WaterSplatting: Fast Underwater 3D Scene Reconstruction Using Gaussian Splatting](https://mlsanity.com/paper/2408.08206/) (2024)
- [SeaSplat: Representing Underwater Scenes with 3D Gaussian Splatting and a Physically Grounded Image Formation Model](https://mlsanity.com/paper/2409.17345/) (2024)
- [FathomNet: A global image database for enabling artificial intelligence in the ocean](https://mlsanity.com/paper/2109.14646/) (2021)
- [UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction](https://mlsanity.com/paper/2410.01517/) (2024)
- [MarineGPT: Unlocking Secrets of Ocean to the Public](https://mlsanity.com/paper/2310.13596/) (2023)
- [Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery](https://mlsanity.com/paper/2202.13487/) (2022)
- [CoralVOS: Dataset and Benchmark for Coral Video Segmentation](https://mlsanity.com/paper/2310.01946/) (2023)
- [UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer](https://mlsanity.com/paper/2310.20210/) (2023)
- [The Coralscapes Dataset: Semantic Scene Understanding in Coral Reefs](https://mlsanity.com/paper/2503.20000/) (2025)
- [Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning](https://mlsanity.com/paper/2309.12804/) (2023)
- [Image Labels Are All You Need for Coarse Seagrass Segmentation](https://mlsanity.com/paper/2303.00973/) (2023)
- [Multi-species Seagrass Detection and Classification from Underwater Images](https://mlsanity.com/paper/2009.09924/) (2020)
- [Reconfigurable Robots for Scaling Reef Restoration](https://mlsanity.com/paper/2205.04612/) (2022)
- [Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments](https://mlsanity.com/paper/2503.04096/) (2025)
- [Fast Underwater Scene Reconstruction using Multi-View Stereo and Physical Imaging](https://mlsanity.com/paper/2501.11884/) (2025)
- [A drone-based framework for coral habitat mapping via weakly supervised segmentation](https://mlsanity.com/paper/2508.18958/) (2025)

## Built on by (most cited first, 3 of 3)

- [Long-Term Mapping of the Douro River Plume with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.03534/) (2025)
- [Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality](https://mlsanity.com/paper/2607.10575/) (2026)
- [A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images](https://mlsanity.com/paper/2608.15363/) (2026)

## Abstract (arXiv)

Marine ecosystems face increasing pressure due to climate change, driving the need for scalable, AI-powered monitoring solutions to inform effective conservation and restoration efforts. This paper examines the rapid emergence of underwater AI as a major research frontier and analyzes the factors that have transformed marine perception from a niche application into a catalyst for AI innovation. We identify three convergent drivers: i) environmental necessity for ecosystem-scale monitoring, ii) democratization of underwater datasets through citizen science platforms, and iii) researcher migration from saturated terrestrial computer vision domains. Our analysis reveals how unique underwater challenges - turbidity, cryptic species detection, expert annotation bottlenecks, and cross-ecosystem generalization - are driving fundamental advances in weakly supervised learning, open-set recognition, and robust perception under degraded conditions. We survey emerging trends in datasets, scene understanding and 3D reconstruction, highlighting the paradigm shift from passive observation toward AI-driven, targeted intervention capabilities. The paper demonstrates how underwater constraints are pushing the boundaries of foundation models, self-supervised learning, and perception, with methodological innovations that extend far beyond marine applications to benefit general computer vision, robotics, and environmental monitoring.