Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2024
|
| Materias: | |
| Acceso en línea: | https://doi.org/10.5281/zenodo.13985741 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866902137565347840 |
|---|---|
| author | Moqadam, Hameed Eisen, Olaf |
| author_facet | Moqadam, Hameed Eisen, Olaf |
| contents | <p><br># README: IRHMapNet Radargram and Mask Patches Dataset</p> <p>## Dataset Overview</p> <p>This dataset contains radargram patches and corresponding mask patches used for training and evaluating the **IRHMapNet** model. The dataset is designed for segmentation of internal reflection horizons (IRHs) from radio-echo sounding data. The data is organized into two directories: radargram patches (`grams_patches`) and mask patches (`masks_patches`), with each patch having dimensions of 512x512 pixels.</p> <p>### Contents</p> <p>- **grams_patches/**: Contains 600 `.csv` files representing radargram patches. Each file is a 512x512 matrix corresponding to a small section of the radargram image.<br>- **masks_patches/**: Contains 600 `.csv` files representing the ground-truth mask patches for segmentation. Each file is a 512x512 binary mask, where `1` indicates the presence of an internal reflection horizon (IRH), and `0` represents background or ice.</p> <p>## Data Format</p> <p>- The files in both directories are named consistently, with matching pairs of radargram and mask patches.<br> - Example: `grams_patches/patch_001.csv` corresponds to `masks_patches/patch_001.csv`.<br>- Each `.csv` file is a comma-separated values (CSV) file containing 512 rows and 512 columns.</p> <p>## Directory Structure</p> <p>```<br>DATA_IRHMapNet/<br>├── grams_patches/ # Radargram patches<br>│ ├── patch_001.csv<br>│ ├── patch_002.csv<br>│ └── ... (600 patches)<br>└── masks_patches/ # Mask patches (Ground truth)<br> ├── patch_001.csv<br> ├── patch_002.csv<br> └── ... (600 patches)<br>```</p> <p>## Usage Instructions</p> <p>1. **Loading the data**: Each `.csv` file can be loaded using standard CSV reading functions in Python, such as `numpy.loadtxt()` or `pandas.read_csv()`.<br> <br> Example in Python using `numpy`:<br> ```python<br> import numpy as np<br> radargram_patch = np.loadtxt('grams_patches/patch_001.csv', delimiter=',')<br> mask_patch = np.loadtxt('masks_patches/patch_001.csv', delimiter=',')<br> ```</p> <p>2. **Model training**: These patches are designed for input into a U-Net or similar convolutional neural network architectures for pixel-wise classification tasks. The radargram patches serve as input, and the mask patches provide the ground-truth labels for training.</p> <p>## License</p> <p>This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to:</p> <p> Share — copy and redistribute the material in any medium or format.<br> Adapt — remix, transform, and build upon the material for any purpose, even commercially.</p> <p>You must give appropriate credit by citing the following publication:</p> <p>**Citation**: Moqadam, H., et al. (2024). Going deeper with deep learning: Automatically tracing internal reflection horizons in ice sheets. *Journal of Geophysical Research: Machine Learning and Computation*. DOI: [insert DOI]</p> <p>## Contact</p> <p>For questions or further information, please contact Hameed Moqadam at [hameed.moqadam@awi.de].<br> Data Curator: Hameed Moqadam<br> Annotator: Hameed Moqadam<br> Data Manager: Hameed Moqadam</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_13985741 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | IRHMapNet Radargram and Mask Patches Dataset. Moqadam, Hameed Eisen, Olaf radargram deep learning internal reflection horizon segmentation radioglaciology U-Net <p><br># README: IRHMapNet Radargram and Mask Patches Dataset</p> <p>## Dataset Overview</p> <p>This dataset contains radargram patches and corresponding mask patches used for training and evaluating the **IRHMapNet** model. The dataset is designed for segmentation of internal reflection horizons (IRHs) from radio-echo sounding data. The data is organized into two directories: radargram patches (`grams_patches`) and mask patches (`masks_patches`), with each patch having dimensions of 512x512 pixels.</p> <p>### Contents</p> <p>- **grams_patches/**: Contains 600 `.csv` files representing radargram patches. Each file is a 512x512 matrix corresponding to a small section of the radargram image.<br>- **masks_patches/**: Contains 600 `.csv` files representing the ground-truth mask patches for segmentation. Each file is a 512x512 binary mask, where `1` indicates the presence of an internal reflection horizon (IRH), and `0` represents background or ice.</p> <p>## Data Format</p> <p>- The files in both directories are named consistently, with matching pairs of radargram and mask patches.<br> - Example: `grams_patches/patch_001.csv` corresponds to `masks_patches/patch_001.csv`.<br>- Each `.csv` file is a comma-separated values (CSV) file containing 512 rows and 512 columns.</p> <p>## Directory Structure</p> <p>```<br>DATA_IRHMapNet/<br>├── grams_patches/ # Radargram patches<br>│ ├── patch_001.csv<br>│ ├── patch_002.csv<br>│ └── ... (600 patches)<br>└── masks_patches/ # Mask patches (Ground truth)<br> ├── patch_001.csv<br> ├── patch_002.csv<br> └── ... (600 patches)<br>```</p> <p>## Usage Instructions</p> <p>1. **Loading the data**: Each `.csv` file can be loaded using standard CSV reading functions in Python, such as `numpy.loadtxt()` or `pandas.read_csv()`.<br> <br> Example in Python using `numpy`:<br> ```python<br> import numpy as np<br> radargram_patch = np.loadtxt('grams_patches/patch_001.csv', delimiter=',')<br> mask_patch = np.loadtxt('masks_patches/patch_001.csv', delimiter=',')<br> ```</p> <p>2. **Model training**: These patches are designed for input into a U-Net or similar convolutional neural network architectures for pixel-wise classification tasks. The radargram patches serve as input, and the mask patches provide the ground-truth labels for training.</p> <p>## License</p> <p>This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to:</p> <p> Share — copy and redistribute the material in any medium or format.<br> Adapt — remix, transform, and build upon the material for any purpose, even commercially.</p> <p>You must give appropriate credit by citing the following publication:</p> <p>**Citation**: Moqadam, H., et al. (2024). Going deeper with deep learning: Automatically tracing internal reflection horizons in ice sheets. *Journal of Geophysical Research: Machine Learning and Computation*. DOI: [insert DOI]</p> <p>## Contact</p> <p>For questions or further information, please contact Hameed Moqadam at [hameed.moqadam@awi.de].<br> Data Curator: Hameed Moqadam<br> Annotator: Hameed Moqadam<br> Data Manager: Hameed Moqadam</p> |
| title | IRHMapNet Radargram and Mask Patches Dataset. |
| topic | radargram deep learning internal reflection horizon segmentation radioglaciology U-Net |
| url | https://doi.org/10.5281/zenodo.13985741 |