Detection of burned areas using deep learning from satellite images
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Updated
Feb 7, 2022 - Jupyter Notebook
Detection of burned areas using deep learning from satellite images
A semi-automatic GEE tool to monitor burned area progression using Sentinel-1 SAR data. Attachment for the article in the IEEE JSTARS by Paluba D. et al. (2024): Tracking burned area progression in an unsupervised manner using Sentinel-1 SAR data in Google Earth Engine https://doi.org/10.1109/JSTARS.2024.3427382
ModL2T: hybrid MODIS and Landsat algorithm in Google Earth Engine for estimating post-monsoon burned area from agricultural fires in northwestern India
This code repository is an attachment for the IGARSS 2023 proceeding paper: Paluba D. et al. (2023): "Unsupervised Burned Area Mapping in Greece: Investigating the impact of precipitation, pre- and post-processing of Sentinel-1 data in Google Earth Engine".
Visualization of burned area satellite data for Australian bush fires
A simple graphic editor that you can draw and calculate the percentage of filled area. It can be used for calculating the percentage of a burned human body area.
Predicted Burned area of forest fires and Turbine yield energy using ANN
Forest Fires Prediction using Neural Networks
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