Deep Learning Models for Wildfire Danger Forecasting
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Updated
Sep 1, 2022 - Python
Deep Learning Models for Wildfire Danger Forecasting
Detection & monitoring platform of wildfires
Download wildfires data from CalFire
Python for Raw Sentinel-2 data (PyRawS) is an open-source software providing utilities to open and process Sentinel 2 RAW data, which corresponds to a decompressed version of Level-0 data with additional metadata. The software is demonstrated on the first Sentinel-2 dataset containing raw data for warm temperature hotspots detection/classification.
Download wildfire hotspots detected by NASA satellites and the Fire Information for Resource Management System (FIRMS)
Download wildfires data from NOAA satellites
Download watch, warning and advisory data from the National Weather Service
Download wildfires data from the National Interagency Fire Center
Live wild fire data visualization, historical data analysis, future fires prediction based on Machine Learning model
Teleconnection-driven vision transformers for improved long-term forecasting
Download wildfire incidents data from InciWeb
Environmental issues reports from Argentina
Implementation of several state-of-the-art Deep Learning models for fire semantic segmentation.
An exploration of wildfires and acres burned in the United States since 1983.
Agent-based modeling 2D wildfire suppression simulator tool built on the mesa framework in Python
A dashboard for visualization of Oregon Wildfire Data from 1961-2019
Wildfire prediction machine learning model
An on-the-ground live feed of updates to keep you prepared for the worst!
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