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Prédiction des distributions de moules d'eau douce

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Moulinette

Mussel prediction from MBES point cloud

1) Prep ground truth data

Remove useless columns, sort by date

cut -d ";" -f 1,2,3,5 data/mussels/merge_of_raw_data.csv | sort -t";" -k4 -r > mussels_sorted_by_date_epsg-8254.csv

Visualize that first 4 lines are not valid -> look at the date

cat mussels_sorted_by_date_epsg-8254.csv | sort -t";" -k4 -r | less

Delete first 4 lines

sed -i '1,4d' mussels_sorted_by_date_epsg-8254.csv

Keep latest unique data points

cut -d ";" -f 1-3 mussels_sorted_by_date_epsg-8254.csv | sort -k1,1 -k2,2 --unique > mussels_epsg-8254.csv

2) Train regression model

python3 src/train_model.py data/train_data/training_data.txt

3) Generate hackel file

Mbes file must be in a projected or orthogonal reference system on a 1*1 M grid

The utility soundings_generate_features is from Benthinc_classifier : https://github.com/CIDCO-dev/BenthicClassifier

cat MBES_FILE.txt | ./soundings_generate_features 10 > outputFilePath.hackel

6) Apply model

python3 apply_model.py trained_mussel_regression.model FILE.hackel > outputFilePath_result.csv

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