Skip to content

Current available solutions eg #WorldCover apply inconsistent "cropland" definitions from #FAOSATA’s and are rarely up to date. This work focused on building cost effective classification for cropland mapping for #Sudan, #Iran & #Afghanistan regions. For Afghanistan we focused on temporal classification while for the rest year-long classification

Notifications You must be signed in to change notification settings

JuliusFx131/GEO-AI-Challenge-for-Cropland-Mapping-Challenge

 
 

Repository files navigation

Order of Executing the .ipynb Files:

1.Ensure you are working in the Anaconda environment on your PC. All files provided for this work should be in same location


2.Begin by running the "1.a) Data Extraction Prep -Split Into Countries.ipynb" file first.


-This step is necessary to generate the each country's train and test csv files, which are used in the subsequent "1.b) Data Extraction - Final Step_(NOT COLAB).ipynb" file.

3. Proceed to run the "1.b) Data Extraction - Final Step_(NOT COLAB).ipynb" file.

-This step is necessary to pull sentinel bands for each country for both train and test files 


4. Proceed to run the "2.e) GRIDSEARCHED - 0.913.ipynb" file.


5. For the final submission on Zindi, use the output from the last step, specifically the "1.lgbm.csv".

About

Current available solutions eg #WorldCover apply inconsistent "cropland" definitions from #FAOSATA’s and are rarely up to date. This work focused on building cost effective classification for cropland mapping for #Sudan, #Iran & #Afghanistan regions. For Afghanistan we focused on temporal classification while for the rest year-long classification

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%