# FireSR Dataset

## Overview

**FireSR** is a dataset designed for the super-resolution and segmentation of wildfire-burned areas. It includes data for all wildfire events in Canada from 2017 to 2023 that exceed 2000 hectares in size, as reported by the National Burned Area Composite (NBAC). The dataset aims to support high-resolution daily monitoring and improve wildfire management using machine learning techniques.

## Dataset Structure

The dataset is organized into several directories, each containing data relevant to different aspects of wildfire monitoring:

- **S2**: Contains Sentinel-2 images.
- **pre**: Pre-fire Sentinel-2 images (high resolution).
- **post**: Post-fire Sentinel-2 images (high resolution).

- **mask**: Contains NBAC polygons, which serve as ground truth masks for the burned areas.
- **pre**: Burned area labels from the year before the fire, using the same spatial bounds as the fire events of the current year.
- **post**: Burned area labels corresponding to post-fire conditions.

- **MODIS**: Contains post-fire MODIS images (lower resolution).

- **LULC**: Contains land use/land cover data from ESRI Sentinel-2 10-Meter Land Use/Land Cover (2017-2023).

- **Daymet**: Contains weather data from Daymet V4: Daily Surface Weather and Climatological Summaries.

### File Naming Convention

Each GeoTIFF (.tif) file is named according to the format: `CA_

### Directory Structure

The dataset is organized as follows:

```
FireSR/
│
├── dataset/
│ ├── S2/
│ │ ├── post/
│ │ │ ├── CA_2017_AB_204.tif
│ │ │ ├── CA_2017_AB_2418.tif
│ │ │ └── ...
│ │ ├── pre/
│ │ │ ├── CA_2017_AB_204.tif
│ │ │ ├── CA_2017_AB_2418.tif
│ │ │ └── ...
│ ├── mask/
│ │ ├── post/
│ │ │ ├── CA_2017_AB_204.tif
│ │ │ ├── CA_2017_AB_2418.tif
│ │ │ └── ...
│ │ ├── pre/
│ │ │ ├── CA_2017_AB_204.tif
│ │ │ ├── CA_2017_AB_2418.tif
│ │ │ └── ...
│ ├── MODIS/
│ │ ├── CA_2017_AB_204.tif
│ │ ├── CA_2017_AB_2418.tif
│ │ └── ...
│ ├── LULC/
│ │ ├── CA_2017_AB_204.tif
│ │ ├── CA_2017_AB_2418.tif
│ │ └── ...
│ ├── Daymet/
│ │ ├── CA_2017_AB_204.tif
│ │ ├── CA_2017_AB_2418.tif
│ │ └── ...
```

### Spatial Resolution and Channels

- **Sentinel-2 (S2) Images**: 20 meters (Bands: B12, B8, B4)
- **MODIS Images**: 250 meters (Bands: B7, B2, B1)
- **NBAC Burned Area Labels**: 20 meters (1 channel, binary classification: burned/unburned)
- **Daymet Weather Data**: 1000 meters (7 channels: dayl, prcp, srad, swe, tmax, tmin, vp)
- **ESRI Land Use/Land Cover Data**: 10 meters (1 channel with 9 classes: water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, rangeland)

**Daymet Weather Data**: The Daymet dataset includes seven channels that provide various weather-related parameters, which are crucial for understanding and modeling wildfire conditions:

| Name | Units | Min | Max | Description |

|------|-------|-----|-----|-------------|

| dayl | seconds | 0 | 86400 | Duration of the daylight period, based on the period of the day during which the sun is above a hypothetical flat horizon. |

| prcp | mm | 0 | 544 | Daily total precipitation, sum of all forms converted to water-equivalent. |

| srad | W/m^2 | 0 | 1051 | Incident shortwave radiation flux density, averaged over the daylight period of the day. |

| swe | kg/m^2 | 0 | 13931 | Snow water equivalent, representing the amount of water contained within the snowpack. |

| tmax | °C | -60 | 60 | Daily maximum 2-meter air temperature. |

| tmin | °C | -60 | 42 | Daily minimum 2-meter air temperature. |

| vp | Pa | 0 | 8230 | Daily average partial pressure of water vapor. |

**ESRI Land Use/Land Cover Data**: The ESRI 10m Annual Land Cover dataset provides a time series of global maps of land use and land cover (LULC) from 2017 to 2023 at a 10-meter resolution. These maps are derived from ESA Sentinel-2 imagery and are generated by Impact Observatory using a deep learning model trained on billions of human-labeled pixels. Each map is a composite of LULC predictions for 9 classes throughout the year, offering a representative snapshot of each year.

| Class Value | Land Cover Class |

|-------------|------------------|

| 1 | Water |

| 2 | Trees |

| 4 | Flooded Vegetation |

| 5 | Crops |

| 7 | Built Area |

| 8 | Bare Ground |

| 9 | Snow/Ice |

| 10 | Clouds |

| 11 | Rangeland |

## Usage Tutorial

To help users get started with FireSR, we provide a comprehensive tutorial with scripts for data extraction and processing. Below is an example workflow:

### Step 1: Extract FireSR.tar.gz

```bash
tar -xvf FireSR.tar.gz
```

### Step 2: Tiling the GeoTIFF Files

The dataset contains high-resolution GeoTIFF files. For machine learning models, it may be useful to tile these images into smaller patches. Here's a Python script to tile the images:

```python
import rasterio
from rasterio.windows import Window
import os

def tile_image(image_path, output_dir, tile_size=128):
with rasterio.open(image_path) as src:
for i in range(0, src.height, tile_size):
for j in range(0, src.width, tile_size):
window = Window(j, i, tile_size, tile_size)
transform = src.window_transform(window)
outpath = os.path.join(output_dir, f"{os.path.basename(image_path).split('.')[0]}_{i}_{j}.tif")
with rasterio.open(outpath, 'w', driver='GTiff', height=tile_size, width=tile_size, count=src.count, dtype=src.dtypes[0], crs=src.crs, transform=transform) as dst:
dst.write(src.read(window=window))

# Example usage
tile_image('FireSR/dataset/S2/post/CA_2017_AB_204.tif', 'tiled_images/')
```

### Step 3: Loading Data into a Machine Learning Model

After tiling, the images can be loaded into a machine learning model using libraries like PyTorch or TensorFlow. Here's an example using PyTorch:

```python
import torch
from torch.utils.data import Dataset
from torchvision import transforms
import rasterio

class FireSRDataset(Dataset):
def _init_(self, image_dir, transform=None):
self.image_dir = image_dir
self.transform = transform
self.image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.tif')]

def _len_(self):
return len(self.image_paths)

def _getitem_(self, idx):
image_path = self.image_paths[idx]
with rasterio.open(image_path) as src:
image = src.read()
if self.transform:
image = self.transform(image)
return image

# Example usage
dataset = FireSRDataset('tiled_images/', transform=transforms.ToTensor())
dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)
```

## License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material as long as appropriate credit is given.

## Contact

For any questions or further information, please contact:
- Name: Eric Brune
- Email: ebrune@kth.se

A national map of Canadian Fire Behaviour Prediction (FBP) Fuel Types (FT) developed from public data sources. The resolution of the raster grid is 30m, classified from the Spatialized Canadian National Forest Inventory (SCANFI) dataset, ecozones of Canada, and the National Burned Area Composite (NBAC). The purpose of the dataset is to characterize Canadian forests into fuel types for use in Fire Behaviour Prediction calculations as well as for situational awareness of national fire potential.

This layer is a combination of fire polygons from the National Burned Area Composite from 1986-2017 dataset, which maps fire perimeters more precisely and excludes water bodies and unburned patches within fire perimeters, combined with fires from 1985 and earlier from the Canadian National Fire Database. Fire polygons were clipped to the NT1 boreal caribou range boundary (2016 GNWT-ENR version).

Database of wildfire evacuations across the forested regions of Canada from 1980 to 2019. The database provides the data evaluated in the publication titled, “Wildfire evacuation patterns and syndromes across Canada’s forested regions,” which was submitted to Ecological Applications in November, 2021. The data file provides information on each documented wildfire evacuation and the characteristics of the fire that most likely led to the evacuation. The information on wildfire evacuations includes the location, the estimated population, whether it is a First Nations reserve, the estimated number of evacuess in five broad categories, the date that the evacuation order was issued, the date the order ended, and the reason why the order was issued. This information was compiled from more than 2,000 news reports, which were found using keyword searches in the Canadian Reference Centre, Canadian Newsstand, Canadian Research Index, Canadian Business and Current Affairs, and Proquest. Additional data were acquired by contacting provincial and territorial emergency management agencies and the Royal Canadian Mounted Police, as described in Beverly and Bothwell (2011; Wildfire evacuations in Canada 1980-2007; Natural Hazards 59:571-596). The fire characteristics include the fire size, ignition source, day of year on which it was first reported, and the database from which these values were derived. The databases for the original fire data include the National Burned Area Composite (NBAC), the National Fire Database (NFDB) fire polygon data, and the NFDB fire point data. The fire databases are available to the public at the CWFIS datamart (https://cwfis.cfs.nrcan.gc.ca/datamart).