How You Can Build Your Own Worldwide Wildfire Data Collection and Host on Kaggle

Learn how to build a comprehensive worldwide wildfire data collection and host it on Kaggle in this detailed tutorial. Explore techniques for harvesting data from NASA, creating datasets for specific countries like the USA, Canada, and Nepal, and visualizing wildfire data using tools such as mapboxgl, matplotlib, and Kepler.gl. Develop a Streamlit app to display wildfire information for any country or region by year. Gain insights into data collection, filtering, feature selection, and various visualization methods for geospatial data. The tutorial also covers pushing code to GitHub and hosting datasets on Kaggle, providing a complete workflow for building and sharing wildfire data projects.

- Content start
- Topic introduction
- Project Source Code at GitHub
- Dataset hosting at Kaggle Hosting
- Wildfire Dataset Technology
- Tutorial Starts now
- USA wildfire dataset filtering
- Feature selection from wildfire dataset
- USA wildfire dataset saved to disk
- USA wildfire data visualization with Kepler.gl
- Filtering California wildfire data
- Creating Nepal Wildfire dataset
- Kepler.gl tutorial for geospatial data
- Wildfire dataset visualization with mapboxgl
- Wildfire dataset visualization with matplotlib
- Streamlit application for wildfire dataset
- Recap
- Code push to GitHub
- Project Completion