Datasets:

deeprealaiml
/

SeafloorAI

	---
	license: cc-by-nc-4.0
	task_categories:
	- image-segmentation
	language:
	- en
	tags:
	- geospatial
	- sonar
	- seafloor
	- bathymetry
	- backscatter
	- marine-geosciences
	- datasets
	pretty_name: "SeafloorAI"
	size_categories:
	- n>1T
	extra_gated_fields:
	Full Name: text
	Affiliation: text
	Identity:
	type: select
	options:
	- University / Research Institute
	- Commercial Company
	- Government / NGO
	- Individual
	Country: country
	Specific date: date_picker
	I want to use this dataset for:
	type: select
	options:
	- Research
	- Education
	- label: Other
	value: other
	I agree to use this dataset for non-commercial use ONLY: checkbox
	---

	# SeafloorAI: The First Large-Scale AI-Ready Dataset for Seafloor Mapping

	[![Paper](https://img.shields.io/badge/NeurIPS-2024-red)](https://proceedings.neurips.cc/paper_files/paper/2024/hash/274de7d60333c0848f42e18ae97f13e3-Abstract-Datasets_and_Benchmarks_Track.html)

	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

	![dataset_overiew](https://cdn-uploads.huggingface.co/production/uploads/691e3a83488fa3bd99680806/czAUxbPYaFp2MsEMHGM9e.jpeg)

	<!-- - Scale: 62 geo-distributed survey campaigns covering 17,300 km².
	- Size: 696K sonar images paired with 827K annotated segmentation masks.
	- Resolution: 224 × 224 per image.
	- Regions: 9 major geological regions. -->

	### Data Layers
	The dataset includes 11 layers:
	1. Raw Signals: Backscatter, Bathymetry, Slope, Rugosity, Longitude, Latitude.
	2. 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.

	<!-- ### Region-Annotation Overview

	\| Region \| Available Annotations \|
	\|--------\|----------------------\|
	\| region1, region2, region5, region6, region7 \| Sediment (`sed/`), Physiographic Zone (`pzone/`) \|
	\| region3, region4 \| Habitat (`habitat/`), Fault (`fault/`), Fold (`fold/`) \| -->

	### Directory Organization

	```text
	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
	├── fault/ # Splits for fault task
	│ └── region{3,4}/
	│ ├── train.json
	│ ├── val.json
	│ └── test.json
	└── fold/ # Splits for fold 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
	![visualization_region1_0000151_0000562](https://cdn-uploads.huggingface.co/production/uploads/6980c76bccfb4a4ef9cd852b/WhHWKwaziPY5JldgFm3WH.png)

	### Region 3 - Habitat, Fault & Fold
	![visualization_region3_0000437_0000778](https://cdn-uploads.huggingface.co/production/uploads/6980c76bccfb4a4ef9cd852b/89acvsWQ-Hj48ssf0LJwE.png)

	## 💻 Visualization & Dataloader

	### Simple Visualization
	To visualize samples and segmentation masks in the dataset, please refer to [visualization.ipynb](visualization.ipynb).

	### PyTorch Dataset Integration
	To integrate with deep learning workflows, refer to the `SeafloorDataset` implementation in [seafloor_dataset.py](seafloor_dataset.py).

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

	```python
	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:

	```bibtex
	@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.