PiSchool/noa-xai-for-wildfire-forecasting
Code for the School of AI challenge "Explainable AI for Wildfire Forecasting", sponsored by Pi School to help NOA, the National Observatory of Athens, work with Explainable Deep Learning for Wildfire Forecasting.
This project helps forecasters and environmental scientists anticipate future wildfire danger by using satellite and climate data to predict burned areas globally. It takes in various Earth observation data like climate, vegetation, and oceanic indices, and outputs forecasts of where and when wildfires might occur, along with insights into what factors are driving those predictions. This is ideal for those managing fire prevention, land use, or climate impact assessments.
No commits in the last 6 months.
Use this if you need to understand the underlying drivers behind seasonal to sub-seasonal wildfire forecasts across different global regions.
Not ideal if you require very short-term (e.g., daily) or highly localized (e.g., specific forest plot) wildfire predictions.
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Sep 06, 2023
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