HakaiInstitute/mussel-gooseneck-seg-rgb-640

--- license: cc-by-4.0 task_categories: - image-segmentation language: - en tags: - image - geospatial - biology - aerial imagery - remote sensing pretty_name: MusselGooseneckSeg 640 size_categories: - 1K<n<10K --- # 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](https://huggingface.co/docs/datasets/en/installation)) ```python from datasets import load_dataset train_dataset = load_dataset("HakaiInstitute/mussel-gooseneck-seg-rgb-640", split="train") val_dataset = load_dataset("HakaiInstitute/mussel-gooseneck-seg-rgb-640", split="validation") 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 WebDataset, which makes it possible to use the data without downloading it in advance. For instructions on how to do this, please see [WebDataset](https://huggingface.co/docs/hub/en/datasets-webdataset) ## Data characteristics - **Image Format:** TIFF - **Tile Size:** 640x640 pixels - **Train Tile Overlap:** 50% (adjacent chips overlap by 320 pixels in both dimensions) - **Validation Tile Overlap:** None - **Number of Tiles:** 6,967 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 <!-- TODO: Fill in class distribution percentages after computing pixel label statistics --> | Class ID | Class Name | Description | Percentage | | :------- | :------------------- | :---------------------- | :--------: | | 0 | Background | Unclassified areas | TODO | | 1 | Mussels | Mussel bed | TODO | | 2 | Gooseneck Barnacles | Gooseneck barnacle bed | TODO | ## Split information | Split | Data Percentage | Tiles Count | | :--------- | --------------: | ----------: | | Train | 97% | 6,743 | | Validation | 3% | 224 | ## Preprocessing 1. Tiles extracted from source imagery at 640x640 px 2. Training tiles extracted with 50% overlap between adjacent chips 3. Validation tiles extracted with no overlap between chips 4. 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{denouden2026musselgoosenecseg640, author = {Denouden, Taylor and McInnes, William and Guyn, Alex}, title = {MusselGooseneckSeg 640: Semantic Segmentation for Rocky Intertidal Mussel and Gooseneck Barnacle Habitat}, month = April, year = 2026, doi = { 10.57967/hf/8504 }, publisher = {Hakai Institute {\tt [e-mail]}}, howpublished = {\url{https://huggingface.co/datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-640}} } ``` ## 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 - Overlapping training tiles increase the effective training set size but may introduce spatial autocorrelation between nearby chipslicense: cc-by-4.0 task_categories: - 图像分割（image-segmentation） language: - 英语（en） tags: - 图像（image） - 地理空间（geospatial） - 生物学（biology） - 航空影像（aerial imagery） - 遥感（remote sensing） pretty_name: MusselGooseneckSeg 640 size_categories: - 1K<n<10K --- # MusselGooseneckSeg：岩潮带贻贝与茗荷栖息地语义分割数据集 ## 数据集描述 MusselGooseneckSeg是一款基于高分辨率无人机影像（drone imagery）的贻贝与茗荷栖息地语义分割（Semantic Segmentation）数据集，可为岩潮带区域内的贻贝和茗荷提供像素级标注。 - **数据来源**：由哈卡伊研究所（Hakai Institute）采集的影像 ## 任务说明 本数据集专为航空影像中的贻贝与茗荷栖息地语义分割任务设计，需将图像中每个像素划分为以下三类之一：“贻贝”、“茗荷”或“背景”。 ## 使用方法 ### 下载与迭代使用 安装Hugging Face数据集库（[安装指南](https://huggingface.co/docs/datasets/en/installation)） python from datasets import load_dataset train_dataset = load_dataset("HakaiInstitute/mussel-gooseneck-seg-rgb-640", split="train") val_dataset = load_dataset("HakaiInstitute/mussel-gooseneck-seg-rgb-640", split="validation") for sample in train_dataset: x = sample["image.tif"] y = sample["label.tif"] # x和y均为PIL.Image（PIL图像）实例，可直接用于训练循环、PyTorch数据加载器等场景。 # ... ### 从Hugging Face流式读取 本数据集以WebDataset（WebDataset）格式发布，支持无需提前下载即可直接使用数据。具体操作指南请参见[WebDataset](https://huggingface.co/docs/hub/en/datasets-webdataset) ## 数据特性 - **图像格式**：TIFF - **切片尺寸**：640×640像素 - **训练切片重叠率**：50%（相邻切片在两个维度上均重叠320像素） - **验证切片重叠率**：无重叠 - **切片总数**：6967张图像-标注对 ## 标注细节 - **标注方法**：人工平视数字化标注并经人工校验 - **标注格式**：像素级标签以独立掩码图像形式存储 - **标注规范**：每个像素仅分配单个类别标签 ## 类别分布 <!-- TODO: 待计算像素标签统计数据后补充类别分布占比 --> | 类别ID | 类别名称 | 描述 | 占比 | | :----- | :------------------- | :----------------------- | :------: | | 0 | 背景 | 未分类区域 | 待补充 | | 1 | 贻贝 | 贻贝床 | 待补充 | | 2 | 茗荷 | 茗荷床 | 待补充 | ## 划分信息 | 划分集 | 数据占比 | 切片数量 | | :--------- | -------: | -------: | | 训练集 | 97% | 6743 | | 验证集 | 3% | 224 | ## 预处理流程 1. 将原始影像裁剪为640×640像素的切片 2. 训练集切片采用相邻切片50%重叠的方式提取 3. 验证集切片采用无重叠方式提取 4. 为贻贝与茗荷添加像素级标注 ## 许可证信息 本数据集采用知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 License，CC BY 4.0）发布。 ## 伦理考量 - 影像中无任何可识别的个人信息 - 最大程度降低对野生动物与敏感栖息地的干扰 - 航空调查规划阶段已与当地原住民第一民族进行沟通 ## 引用信息 若您在研究中使用本数据集，请引用如下文献： @misc{denouden2026musselgoosenecseg640, author = {Denouden, Taylor and McInnes, William and Guyn, Alex}, title = {MusselGooseneckSeg 640: Semantic Segmentation for Rocky Intertidal Mussel and Gooseneck Barnacle Habitat}, month = April, year = 2026, doi = { 10.57967/hf/8504 }, publisher = {Hakai Institute { t [e-mail]}}, howpublished = {url{https://huggingface.co/datasets/HakaiInstitute/mussel-gooseneck-seg-rgb-640}} } ## 已知局限性 - 影像仅覆盖已知存在贻贝与茗荷栖息地的区域 - 未包含城市或建成环境周边的样本 - 在阴影区域可能存在标注误差，该区域难以区分生物体 - 训练集的重叠切片虽提升了有效训练集规模，但可能在相邻切片间引入空间自相关性