metadata

license: cc-by-4.0
task_categories:
  - image-segmentation
language:
  - en
tags:
  - biology
  - climate
pretty_name: MusselSeg
size_categories:
  - 1K<n<10K

MusselSeg: Semantic Segmentation Dataset for Rocky Intertidal Mussel Habitat

Dataset description

MusselSeg is a large-scale dataset for semantic segmentation of mussel habitat using high resolution drone imagery. It covers coastal mussel habitat located on the central coast of British Columbia, Canada, as well as areas in California, USA and provides pixel-wise annotation for mussel beds.

Source: Imagery collected by the Hakai Institute and University of California Santa Cruz
Geographic Coverage: ~3500m^2
Time Period: 2021-2023

Task description

The dataset is designed for semantic segmentation of mussel habitat in aerial imagery. The task involves assigning each pixel in the image to either the "mussel" class or "other"/"background" class.

Data characteristics

Image Format: GeoTiff
Resolution: mean=0.45cm, stdev=0.20cm
Tile Size: 1024x1024 pixels with 50% overlap (TODO check this)
Number of Tiles: 9972 image and label pairs
Total Dataset Size: 42G

Annotation details

Method: Manual heads-up digitizing with manual verification
Format: Pixel-wise labels stored as separate mask images
Labelling Convention: Each pixel assigned a single class label

Class distribution

Class ID	Class Name	Description	Percentage
0	Background	Unclassified areas	87%
1	Mussels	Mussel bed	13%

Split information

Train: 48% (4834 tiles)
Validation 13% (1277 tiles)
Test 39% (3861 tiles)

Train and Validation split tiles all contain at least 1 pixel in each class. For the Test split, some tiles are entirely the background class.

Splits are created such that tiles from the same source orthomosaic image are not divided across different splits. That is, all tiles derived from the same drone flight are present only in a single split.

Preprocessing

Orthorectification applied to raw imagery
Tiles extracted with 50% overlap
Tiles with no mussels present eliminated for the Train and Validation splits

Licensing information

This dataset is released under the Creative Commons Attribution 4.0 License (CC BY 4.0).

Ethical considerations

No identifiable individuals are present in imagery
Minimized impact on wildlife and sensitive habitats
Engaged with local First Nations in planning aerial surveys

Citation information

If you use this dataset in your research, please cite:

TODO Add authors? Update this

@dataset{MusselSeg2024,
    author = {Denouden, T., McInnes, W.},
    title = {MusselSeg: Semantic Segmentation Dataset for Rocky Intertidal Mussel Habitat},
    year = {2024},
    publisher = {Hugging Face},
    howpublished = {\url{https://huggingface.co/datasets/HakaiInstitute/rgb-mussel-presence-1024-1024}}
}

Baseline model performance

Model	IoU for Mussel Class	Pixel Accuracy
U-Net	TODO	TODO
DeepLabV3+	TODO	TODO

Known limitations

Limited seasonal variation due to imagery being captured primarily in summer months
Imagery only covers areas with known mussel beds
Limited examples of mussel beds in urban or built-up environments

Additional resources

GitHub Repository