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This project empowers communities to actively monitor and report fires using technology and publicly available data. We enhance data accessibility for various stakeholders and focus on using data insights, particularly in understanding aerosol chemistry in different fire types, to predict and prevent wildfires effectively.
National Forest FireBot: a Python script that scrapes incidents for any National Forest using WildCAD's WildWeb or WildWeb-E feature, and reports them to a desired output. Optionally there is a self-service SMS gateway end users can leverage
Automated framework for retrieving and processing Sentinel-2 satellite imagery using the Sentinel Hub API. Focused on analyzing geospatial data, particularly SWIR composites, to visualize wildfire-affected areas and support environmental monitoring.
A Wildfire Detection System that integrates machine learning models with satellite imagery, camera feeds, and weather data to predict and detect wildfires effectively.
This repositories leverages the YOLOv5l model by ultralytics and computer vision algorithms to localize and classify some kind of anomalies that can harm wildlife animals as well as their habitate.
This repository showcases our work on using computer vision to detect wildfires. Explore the code, model, and results of our research on wildfire prevention.
Alaska Project Ideas, mentored by the researchers and collaborators of University of Alaska and supported by open-source entities and enthusiasts in Alaska.