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--- |
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dataset_info: |
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features: |
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- name: name |
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dtype: string |
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- name: canopy |
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sequence: int8 |
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- name: density |
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sequence: float32 |
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- name: slope |
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sequence: int8 |
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- name: shape |
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sequence: int16 |
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length: 2 |
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splits: |
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- name: train |
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num_bytes: 27490487 |
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num_examples: 6 |
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download_size: 7175919 |
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dataset_size: 27490487 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: cc |
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task_categories: |
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- feature-extraction |
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tags: |
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- climate |
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- geology |
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size_categories: |
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- n<1K |
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--- |
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# WildfireSimMaps |
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## Description |
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This is a dataset containing real-world map data for wildfire simulations. |
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The data is in the form of 2D maps with the following features: |
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- `name`: The name of the map data. |
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- `shape`: The shape of the area, in pixels. |
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- `canopy`: The canopy cover in the area, in percentage. |
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- `density`: The density of the area, in percentage. |
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- `slope`: The slope of the area, in degrees. |
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## Quick Start |
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Install the package using pip: |
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```bash |
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pip install datasets |
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``` |
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Then you can use the dataset as follows with **NumPy**: |
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```python |
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import numpy as np |
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from datasets import load_dataset |
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# Load the dataset |
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ds = load_dataset("xiazeyu/WildfireSimMaps", split="train") |
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ds = ds.with_format("numpy") |
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def preprocess_function(examples): |
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# Reshape arrays based on the 'shape' field |
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examples['density'] = [d.reshape(sh) for d, sh in zip(examples['density'], examples['shape'])] |
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examples['slope'] = [s.reshape(sh) for s, sh in zip(examples['slope'], examples['shape'])] |
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examples['canopy'] = [c.reshape(sh) for c, sh in zip(examples['canopy'], examples['shape'])] |
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return examples |
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ds = ds.map(preprocess_function, batched=True, batch_size=None) # Adjust batch_size as needed |
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print(ds[0]) |
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``` |
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To use the dataset with **PyTorch**, you can use the following code: |
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```python |
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import torch |
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from datasets import load_dataset |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# Load the dataset |
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ds = load_dataset("xiazeyu/WildfireSimMaps", split="train") |
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ds = ds.with_format("torch", device=device) |
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def preprocess_function(examples): |
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# Reshape arrays based on the 'shape' field |
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examples['density'] = [d.reshape(sh.tolist()) for d, sh in zip(examples['density'], examples['shape'])] |
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examples['slope'] = [s.reshape(sh.tolist()) for s, sh in zip(examples['slope'], examples['shape'])] |
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examples['canopy'] = [c.reshape(sh.tolist()) for c, sh in zip(examples['canopy'], examples['shape'])] |
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return examples |
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ds = ds.map(preprocess_function, batched=True, batch_size=None) # Adjust batch_size as needed |
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print(ds[0]) |
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``` |
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## Next Steps |
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In order to make practical use of this dataset, you may perform the following tasks: |
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- scale or normalize the data to fit your model's requirements |
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- reshape the data to fit your model's input shape |
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- stack the data into a single tensor if needed |
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- perform data augmentation if needed |
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- split the data into training, validation, and test sets |
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In general, you can use the dataset as you would use any other dataset in your pipeline. |
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And the most important thing is to have fun and learn from the data! |
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## Visualization |
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Density |
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Canopy |
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Slope |
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## License |
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The dataset is licensed under the CC BY-NC 4.0 License. |
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## Contact |
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- Zeyu Xia - [email protected] |
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- Sibo Cheng - [email protected] |
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