SeafloorAI is the first extensive AI-ready dataset for seafloor mapping across 5 geological layers, curated in collaboration with marine scientists.

🌊 Abstract

A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar image dataset publicly available, they suffer from limitations in terms of environment setting and scale. To bridge this gap, we introduce SeafloorAI, the first extensive AI-ready datasets for seafloor mapping across 5 geological layers that is curated in collaboration with marine scientists. The dataset consists of 62 geo-distributed data surveys spanning 17,300 square kilometers, with 696K sonar images and 827K annotated segmentation masks. Each image is provided at a resolution of 224 × 224 pixels.

📊 Dataset Overview

Data Layers

The dataset includes 11 layers:

Raw Signals: Backscatter, Bathymetry, Slope, Rugosity, Longitude, Latitude.
Annotations: Sediment, Physiographic Zone, Habitat, Fault, Fold.

📂 Dataset Structure

The dataset is organized by region with corresponding input signals and annotation layers. Each region contains multi-channel input data and task-specific annotations.

Directory Organization

SeafloorAI/
├── region{1,2,5,6,7}/          # Regions with sediment & physiographic zone
│   ├── input/                  # 6-channel input signals
│   │   └── region*_*.npy       # Shape: (6, 224, 224)
│   ├── sed/                    # Sediment annotations
│   │   └── region*_*.npy       # Shape: (224, 224)
│   └── pzone/                  # Physiographic zone annotations
│       └── region*_*.npy       # Shape: (224, 224)
│
├── region{3,4}/                # Regions with habitat, fault & fold
│   ├── input/                  # 6-channel input signals
│   │   └── region*_*.npy       # Shape: (6, 224, 224)
│   ├── habitat/                # Habitat annotations
│   │   └── region*_*.npy       # Shape: (224, 224)
│   ├── fault/                  # Fault annotations
│   │   └── region*_*.npy       # Shape: (224, 224)
│   └── fold/                   # Fold annotations
│       └── region*_*.npy       # Shape: (224, 224)
│
└── split/                      # Train/validation/test splits
    ├── sed/                    # Splits for sediment task
    │   └── region{1,2,5,6,7}/
    │       ├── train.json
    │       ├── val.json
    │       └── test.json
    ├── pzone/                  # Splits for physiographic zone task
    │   └── region{1,2,5,6,7}/
    │       ├── train.json
    │       ├── val.json
    │       └── test.json
    └── habitat/                # Splits for habitat task
        └── region{3,4}/
            ├── train.json
            ├── val.json
            └── test.json

Data Format Details

Input Files (input/):

6-channel NumPy arrays with shape (6, 224, 224)
Channels: [backscatter, bathymetry, slope, rugosity, longitude, latitude]
Naming: region{N}_{row}_{col}.npy

Annotation Files:

Single-channel NumPy arrays with shape (224, 224)
Integer labels corresponding to class indices
Naming matches corresponding input file

Split Files:

JSON files containing lists of sample identifiers
Organized by annotation type and region

🖼️ Samples

Region 1 - Sediment & Physiographic Zone

Region 3 - Habitat, Fault & Fold

💻 Visualization & Dataloader

Simple Visualization

To visualize samples and segmentation masks in the dataset, please refer to visualization.ipynb.

PyTorch Dataset Integration

To integrate with deep learning workflows, refer to the SeafloorDataset implementation in seafloor_dataset.py.

The following example demonstrates how to import and use it with a PyTorch DataLoader:

from torch.utils.data import DataLoader
from seafloor_dataset import SeafloorDataset

# Initialize Dataset
dataset = SeafloorDataset(
    data_path='./SeafloorAI',
    anno_path='./SeafloorAI/split',
    layer='sed',
    regions=['region1'],
    split='train',
    input_transform=None,  # Optional: transforms for input
    mask_transform=None    # Optional: transforms for mask
)

# Create DataLoader
loader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4)

📋 ToDo

Release fault and fold labels for region3 and region4
Release unlabeled data for region8 and region9
Release SeafloorGenAI Dataset

📜 Citation

If you use the SeafloorAI dataset in your research, please cite the following paper:

@inproceedings{nguyen2024seafloorai,
  title={SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey}, 
  author={Kien X. Nguyen and Fengchun Qiao and Arthur Trembanis and Xi Peng},
  booktitle={Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track},
  year={2024}
}

📧 Contact & Acknowledgments

We would like to acknowledge the support from USGS and NOAA for providing the raw survey data.

For questions regarding the dataset, please open a new discussion in the Community tab.

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