metadata

license: cc-by-4.0
task_categories:
  - image-segmentation
language:
  - en
tags:
  - image
  - geospatial
  - biology
  - aerial imagery
  - remote sensing
pretty_name: MusselSeg
size_categories:
  - 1K<n<10K

MusselSeg: Semantic Segmentation 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: ~3500m²
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 "background" (i.e. not mussels) class.

Usage

Download and iterate

Install the HuggingFace datasets library (instructions)

from datasets import load_dataset

train_dataset = load_dataset("HakaiInstitute/mussel-seg-1024-1024", split="train")
val_dataset = load_dataset("HakaiInstitute/mussel-seg-1024-1024", split="validation")
test_dataset  = load_dataset("HakaiInstitute/mussel-seg-1024-1024", split="test")

for sample in train_dataset:
    x = sample["img.tif"]
    y = sample["seg.tif"]
    # x and y are `PIL.Image` instances, ready to feed into a training loop, PyTorch dataloader, etc.

    # ...

Streaming from HuggingFace

This data is released as a WebDatasets, which makes it possible to use the data without downloading it in advance. For instructions on how to do this, please see WebDataset

Data characteristics

Image Format: GeoTiff
Resolution: mean=0.45cm, stdev=0.20cm
Tile Size: 1024x1024 pixels with 50% overlap
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

Split	Data Percentage	Tiles Count	Mussel Pixels
Train	48%	4834	18.4%
Validation	13%	1277	16.7%
Test	39%	3861	4.3%

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. If ignoring the test split tiles which contain only background pixels, the split percentages instead become 70/17/13 for the train/validation/test splits, respectively.

Splits are created such that tiles from the same source orthomosaic image are not divided across different splits. That is, all tiles 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:

@misc{denouden2024musselseg,
  author = {Denouden, Taylor and McInnes, William and Ammann, Karah and Fletcher, Nathaniel},
  title = {MusselSeg: Semantic Segmentation for Rocky Intertidal Mussel Habitat},
  month = July,
  year = 2024,
  doi = { 10.57967/hf/2760 },
  publisher = {Hakai Institute {\tt [email protected]}},
  howpublished = {\url{https://huggingface.co/datasets/HakaiInstitute/mussel-seg-1024-1024}}
}

Known limitations

Limited seasonal variation due to imagery being captured primarily in summer months
Imagery only covers areas with known mussel beds
No examples of mussel beds near urban or built-up environments
Labelling errors may be present in areas with shadows, where it is difficult to distinguish mussels beds