maidacundo/real-time-fire-segmentation-deep-learning
Implementation of a Deep Neural Architecture to perform real-time semantic segmentation of forest fires in aerial imagery captured by drones.
This project helps forestry services and emergency responders quickly detect and localize woodland fires using aerial imagery from drones. It takes in live video feeds or image files from drones and outputs a visual segmentation mask highlighting areas of fire, enabling faster response. This tool is designed for drone operators, fire surveillance teams, and environmental protection agencies.
No commits in the last 6 months.
Use this if you need a real-time system to identify and map forest fires from drone footage with high accuracy.
Not ideal if you require absolute pixel-perfect segmentation accuracy, as some speed optimizations were made at the expense of minor precision.
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42
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Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 21, 2023
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