Closed HakamShams closed 2 years ago
This is correct! We have open-sourced the code for training the models and new datasets https://github.com/Orion-AI-Lab/wildfire_forecasting . I invite you to have a look there for models that also use land cover information.
Ok, thank you!
Could you please explain this:
From 2009-03-06 to 2018-12-31 the number of burned_areas 5715, number_of_fires 608, ignition_points 608 From 2019-01-01 to 2019-12-31 the number of burned_areas 297, number_of_fires 46, ignition_points 46 From 2020-01-01 to 2019-12-26 the number of burned_areas 412, number_of_fires 89, ignition_points 89
This is inconsistent with the numbers reported in the paper: ""Positive examples (pixels contained in burned areas) are all included in the datasets..... We do a time split using the years 2009-2018 for training, 2019 for validation and 2020 for testing. All the datasets consist of 15731 training (10380 non-fire, 5351 fire), 1337 validation (1075 non-fire,262 fire), and 1286 testing (975 non-fire, 311 fire) examples""?
Hello @HakamShams:
Thanks, this info helps :)
Thank you for your work and for the dataset!
Could you please check the following:
And the static ones are: ['dem_mean', 'aspect_mean', 'slope_mean', 'roads_density_2020', 'population_density']
So overall you used 13 dynamic and 5 static features.
The inputs for a model (e.g., CNN) are all available features for day X. And the target is the variable burned_areas for day X+1 and maybe a comparison with the variable fwi for day X.
You didn't use any pixel from the land cover [sea, water bodies, urban areas] during training/testing.
Is this correct?