Update README.md

README.md

@@ -23,4 +23,118 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc
+task_categories:
+- feature-extraction
+tags:
+- climate
+- geology
+size_categories:
+- n<1K
 ---
+
+# WildfireSimMaps
+
+## Description
+
+This is a dataset containing real-world map data for wildfire simulations. 
+The data is in the form of 2D maps with the following features:
+
+- `name`: The name of the map data.
+- `shape`: The shape of the area, in pixels.
+- `canopy`: The canopy cover in the area, in percentage.
+- `density`: The density of the area, in percentage.
+- `slope`: The slope of the area, in degrees.
+
+## Quick Start
+
+Install the package using pip:
+
+```bash
+pip install datasets
+```
+
+Then you can use the dataset as follows with **NumPy**:
+
+```python
+import numpy as np
+from datasets import load_dataset
+
+# Load the dataset
+ds = load_dataset("xiazeyu/WildfireSimMaps", split="train")
+ds = ds.with_format("numpy")
+
+def preprocess_function(examples):
+    # Reshape arrays based on the 'shape' field
+    examples['density'] = [d.reshape(sh) for d, sh in zip(examples['density'], examples['shape'])]
+    examples['slope'] = [s.reshape(sh) for s, sh in zip(examples['slope'], examples['shape'])]
+    examples['canopy'] = [c.reshape(sh) for c, sh in zip(examples['canopy'], examples['shape'])]
+    
+    return examples
+
+ds = ds.map(preprocess_function, batched=True, batch_size=None)  # Adjust batch_size as needed
+
+print(ds[0])
+```
+
+To use the dataset with **PyTorch**, you can use the following code:
+
+```python
+import torch
+from datasets import load_dataset
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the dataset
+ds = load_dataset("xiazeyu/WildfireSimMaps", split="train")
+ds = ds.with_format("torch", device=device)
+
+def preprocess_function(examples):
+    # Reshape arrays based on the 'shape' field
+    examples['density'] = [d.reshape(sh.tolist()) for d, sh in zip(examples['density'], examples['shape'])]
+    examples['slope'] = [s.reshape(sh.tolist()) for s, sh in zip(examples['slope'], examples['shape'])]
+    examples['canopy'] = [c.reshape(sh.tolist()) for c, sh in zip(examples['canopy'], examples['shape'])]
+    
+    return examples
+
+ds = ds.map(preprocess_function, batched=True, batch_size=None)  # Adjust batch_size as needed
+
+print(ds[0])
+```
+
+## Next Steps
+
+In order to make practical use of this dataset, you may perform the following tasks:
+
+- scale or normalize the data to fit your model's requirements
+- reshape the data to fit your model's input shape
+- stack the data into a single tensor if needed
+- perform data augmentation if needed
+- split the data into training, validation, and test sets
+
+In general, you can use the dataset as you would use any other dataset in your pipeline.
+
+And the most important thing is to have fun and learn from the data!
+
+## Visualization
+
+Density
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461cc67ddb3aaa43c8afef3/RLpWQ0G3Nqfxg-5gJh4YV.png)
+
+Canopy
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461cc67ddb3aaa43c8afef3/LeJoly6Xo8IhoX2WmdXIU.png)
+
+Slope
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461cc67ddb3aaa43c8afef3/lkSHHZs9hjR0Yn0Nedl6x.png)
+
+## License
+
+The dataset is licensed under the CC BY-NC 4.0 License.
+
+## Contact
+
+- Zeyu Xia - yxn7cj@virginia.edu
+- Sibo Cheng - sibo.cheng@imperial.ac.uk