HakaiInstitute/mussel-gooseneck-rgb
Image Segmentation • Updated • 1
Datasets:
image.tif imagewidth (px) 1.02k 1.02k | label.tif imagewidth (px) 1.02k 1.02k | __key__ stringlengths 9 17 | __url__ stringclasses 14
values |
|---|---|---|---|
U0637_random1_0 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_1 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_10 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_11 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_12 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_13 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_14 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_15 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_16 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_17 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_18 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_19 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_2 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_20 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_21 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_22 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_23 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_24 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_25 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_26 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_27 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_28 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_29 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_3 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_30 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_31 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_32 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_33 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_34 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_35 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_36 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_37 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_38 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_39 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_4 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_40 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_41 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_42 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_43 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_44 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_45 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_46 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_47 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_48 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_49 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_5 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_50 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_51 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_52 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_53 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_54 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_55 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_6 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_7 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_8 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0637_random1_9 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0641_random1_0 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0641_random1_1 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0641_random1_10 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0641_random1_11 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000000.tar | ||
U0641_random1_12 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_13 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_14 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_15 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_16 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_17 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_18 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_19 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_2 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_20 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_21 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_22 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_23 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_24 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_25 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_26 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_27 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_28 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_29 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_3 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_30 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_31 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_32 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_33 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_34 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_36 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_37 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_38 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_39 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_4 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_40 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_41 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_5 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_6 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_7 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_8 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0641_random1_9 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0757_North_1 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0757_North_100 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar | ||
U0757_North_101 | hf://datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024@bf4a5d5220ab75c81910b5b44780f7821977da20/train-000001.tar |
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MusselGooseneckSeg: Semantic Segmentation for Rocky Intertidal Mussel and Gooseneck Barnacle Habitat
Dataset description
MusselGooseneckSeg is a dataset for semantic segmentation of mussel and gooseneck barnacle habitat using high resolution drone imagery. It provides pixel-wise annotation for mussels and gooseneck barnacles in rocky intertidal zones.
- Source: Imagery collected by the Hakai Institute
Task description
The dataset is designed for semantic segmentation of mussel and gooseneck barnacle habitat in aerial imagery. The task involves assigning each pixel in the image to one of three classes: "mussel", "gooseneck barnacle", or "background".
Usage
Download and iterate
Install the HuggingFace datasets library (instructions)
from datasets import load_dataset
train_dataset = load_dataset("HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024", split="train")
val_dataset = load_dataset("HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024", split="validation")
test_dataset = load_dataset("HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024", split="test")
for sample in train_dataset:
x = sample["image.tif"]
y = sample["label.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: TIFF
- Tile Size: 1024x1024 pixels
- Tile Overlap: None
- Number of Tiles: 909 image and label pairs
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 | 85.15% |
| 1 | Mussels | Mussel bed | 8.08% |
| 2 | Gooseneck Barnacles | Gooseneck barnacle bed | 6.78% |
Split information
| Split | Data Percentage | Tiles Count |
|---|---|---|
| Train | 89% | 811 |
| Validation | 6% | 55 |
| Test | 5% | 46 |
Preprocessing
- Tiles extracted from source imagery
- Pixel-wise annotations applied for mussels and gooseneck barnacles
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{denouden2024musselgoosenecseg,
author = {Denouden, Taylor and McInnes, William and Guyn, Alex},
title = {MusselGooseneckSeg: Semantic Segmentation for Rocky Intertidal Mussel and Gooseneck Barnacle Habitat},
month = February,
year = 2026,
doi = { 10.57967/hf/7792 },
publisher = {Hakai Institute {\tt data@hakai.org}},
howpublished = {\url{https://huggingface.co/datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024}}
}
Known limitations
- Imagery only covers areas with known mussel and gooseneck barnacle habitat
- No examples near urban or built-up environments
- Labelling errors may be present in areas with shadows, where it is difficult to distinguish organisms
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Models trained or fine-tuned on HakaiInstitute/mussel-gooseneck-seg-rgb-1024-1024