mnpinto/banet
A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images
This project helps environmental scientists and disaster response teams map and date burned areas. It takes daily sequences of multi-spectral satellite images and active fire data to produce detailed maps showing where fires have occurred and when, even for areas with low burn severity. This tool is for professionals in wildfire management, climate research, and remote sensing.
No commits in the last 6 months. Available on PyPI.
Use this if you need to accurately identify and pinpoint the timing of burned areas across large regions using satellite imagery, especially if you require near-real-time results.
Not ideal if you lack access to VIIRS 750m satellite data or do not have the technical expertise to handle data downloads and run command-line scripts.
Stars
43
Forks
14
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Jul 16, 2023
Commits (30d)
0
Dependencies
13
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