prodramp / wildfire

Wildfire Project

Wildfire - Learn and Protect with technology

Wildfire Project - Create solutions in AML, Streamlit, CoreUI, ChakraUI

YouTube Video:

• Predicting Wildfire Hotspots: Combating California wildfire problem with data and AI/ML
  • https://www.youtube.com/watch?v=mlUcDCKSbOs
• Worldwide Wildfire Data Collection: Combating worldwide wildfire problem with data and AI/ML
  • https://youtu.be/Hf04iW-lT-A

Dataset Source Info

• Wildfire data is collected from two instruments (MODIS and VIIRS):
  • Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua and Terra satellites,
  • Visible Infrared Imaging Radiometer Suite (VIIRS) aboard S-NPP and NOAA 20 (formally known as JPSS-1)
• The MODIS data is available from
  • November 2000 (for Terra)
  • July 2002 (for Aqua).
• VIIRS S-NPP 375 m data is available
  • from January 2012 to the present
  • VIIRS NOAA-20 375 m data is available from January 2020

Dataset creation

Create wildfire dataset for any country by your own using NASA MODIS satellite data

• Learn how to create dataset

Wildfire Hotspot Prediction

CA Wildfire Hotspot Visualization

Streamlit application to visualization wildfire data for any country by year

• Get the App and try it

California Wildfire Dataset Zip files (until March 25th 2022)

• Train (2000 - 2019) - Total 1071252 records in 12 columns
  • https://github.com/prodramp/wildfire/raw/main/california-data/ca_fire_train.csv.zip
• Validation Dataset (2020 and 2021) - Total 117936 records in 12 columns
  • https://github.com/prodramp/wildfire/raw/main/california-data/ca_fire_valid.csv.zip
• Test Dataset (Jan - March 2022) - Total 14742 records in 12 columns
  • https://github.com/prodramp/wildfire/raw/main/california-data/ca_fire_test.csv.zip

Resources:

• https://medium.com/ibm-data-ai/predicting-australian-wildfires-with-weather-forecast-data-8d1cc983c863
• https://github.com/Call-for-Code/Spot-Challenge-Wildfires
• https://h2o.ai/wildfire/
• https://github.com/h2oai/challenge-wildfires/blob/main/notebook/DataPreparation.ipynb
• https://github.com/mapbox/mapboxgl-jupyter
• https://www.bigendiandata.com/2017-06-27-Mapping_in_Jupyter/