# A drone-based framework for coral habitat mapping via weakly supervised segmentation

arXiv: https://arxiv.org/abs/2508.18958 · HTML: https://mlsanity.com/paper/2508.18958/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 62 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers](https://mlsanity.com/paper/2203.02664/) (2022)
- [Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook](https://mlsanity.com/paper/2312.05391/) (2023)
- [A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains](https://mlsanity.com/paper/1912.11186/) (2019)
- [SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement](https://mlsanity.com/paper/2502.06756/) (2025)
- [Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2406.11189/) (2024)
- [Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery](https://mlsanity.com/paper/2202.13487/) (2022)
- [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)
- [Combining Photogrammetric Computer Vision and Semantic Segmentation for Fine-grained Understanding of Coral Reef Growth under Climate Change](https://mlsanity.com/paper/2212.04132/) (2022)

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

- [AI-Driven Marine Robotics: Emerging Trends in Underwater Perception and Ecosystem Monitoring](https://mlsanity.com/paper/2509.01878/) (2025)

## Abstract (arXiv)

Obtaining pixel-level annotations over large spatial extents remains a major bottleneck for deploying machine learning in ecological applications. Here we present a multi-scale weakly supervised semantic segmentation (WSSS) framework that enables training high-resolution segmentation models from dense, classification-based outputs. Our method combines fine-scale, multi-label predictions from underwater imagery with broad-coverage aerial data. We convert these point-level classifications into coarse supervision masks that can be used to train a semantic segmentation model on Unmanned Aerial Vehicle (UAV) orthophotos. A second training step using the model's own refined predictions is then used to further improve spatial accuracy without requiring additional annotations. We demonstrate the approach on coral reef imagery, enabling large-area segmentation of coral morphotypes and illustrating its flexibility in integrating new classes. The final model achieves 86.07% pixel accuracy and 52.23% mean Intersection over Union (mIoU) on manually annotated reef zones, demonstrating that accurate large-scale coral segmentation can be obtained without pixel-level annotations. By bridging image classification and segmentation across scales and modalities, this method provides an efficient solution for deploying segmentation models in settings where annotations are unavailable and opens opportunities for scalable, efficient monitoring in ecology and beyond.