A Large-Scale Dataset for Active Fire Detection/Segmentation (Landsat-8)

Citation Author(s):
Gabriel Henrique de Almeida
Pereira
UTFPR
Andre Minoro
Fusioka
UTFPR
Bogdan Tomoyuki
Nassu
UTFPR
Rodrigo
Minetto
UTFPR
Submitted by:
GABRIEL PEREIRA
Last updated:
Sat, 07/04/2020 - 18:41
DOI:
10.21227/t9gn-y009
Data Format:
*.tif
Links:
Description and codes
License:
Creative Commons Attribution
6318 Views
Categories:
Artificial Intelligence
Computer Vision
Image Processing
Machine Learning
Remote Sensing
Geoscience and Remote Sensing
Climate Change/Environmental
Keywords:
Active fire detection; Wildfire segmentation; Wildfire dataset; Landsat-8 images; South America
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Abstract 

This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band). We cropped the original Landsat-8 scenes into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016). 

Instructions: 

The images in our dataset are in georeferenced TIFF (geotiff) format with 10 bands. We cropped the original Landsat-8 scenes (with ~7,600 x 7,600 pixels) into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016). We used the Schroeder conditions to process each patch, producing over 1 million patches with at least one fire pixel and the same amount of patches with no fire pixels, randomly selected from the original images.

The dataset is organized as follow. 

It is divided into South American regions for easy downloading. For each region of South America we have a zip file for images of active fire, its masks, and non-fire images. For example:

 - Uruguay-fire.zip

 - Uruguay-mask.zip

 - Uruguay-nonfire.zip

Within each South American region zip files there are the corresponding zip files to each Landsat-8 WRS (Worldwide Reference System). For example:

- Uruguay-fire.zip;

      - 222083.zip

      - 222084.zip

      - 223082.zip

      - 223083.zip

      - 223084.zip

      - 224082.zip

      - 224083.zip

      - 224084.zip

      - 225081.zip

      - 225082.zip

      - 225083.zip

      - 225084.zip

Within each of these Landsat-8 WRS zip files there are all the corresponding 128x128 image patches for the year 2018. 

 

Comments

very nice dataset, but If GIS layers were uploaded in the same year 2018 it was so great.

Submitted by Mohamad Marjani on Tue, 04/13/2021 - 11:03

thank you for the dataset,are those patches grayscale ?

Submitted by Younes Oubelaid on Mon, 03/14/2022 - 08:38

thank you for the dataset

Submitted by Abhijendra Anand on Mon, 08/29/2022 - 03:03

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