GitHub - JuliusFx131/GEO-AI-Challenge-for-Cropland-Mapping-Challenge: 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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Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
1.a) Data Extraction Prep -Split Into Countries.ipynb		1.a) Data Extraction Prep -Split Into Countries.ipynb
1.b) Data Extraction - Final Step_(NOT COLAB).ipynb		1.b) Data Extraction - Final Step_(NOT COLAB).ipynb
1.c) VISUALIZATION.ipynb		1.c) VISUALIZATION.ipynb
2.e) GRIDSEARCHED - 0.913.ipynb		2.e) GRIDSEARCHED - 0.913.ipynb
Code for Accessing GEE Console.txt		Code for Accessing GEE Console.txt
Presentation Slides.pptx		Presentation Slides.pptx
README.txt		README.txt
Test.csv		Test.csv
Train.csv		Train.csv
requirements.txt		requirements.txt
technical report.pdf		technical report.pdf

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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

zindi.africa/competitions/geo-ai-challenge-for-cropland-mapping-with-satellite-imagery

cropland-mapping earth-observation machine-learning-algorithms sensors-data-collection

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