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