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Olivier Courtin 4 months ago
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# Neat-EO Quality Analysis

This tutorial allow you in few hours (depending on your hardware),
to see how you can validate Quality of a Dataset, easily and at scale.

Check installation and GPU
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neo compare --cover cover.csv --mode list --labels gl/labels --masks gl/masks --max Building QoD 0.80 --out gl/outliers.cover.csv
neo cover --cover gl/outliers.cover.csv --type geojson --out gl/compare/tiles.json

- Pink pixels: predicted by model
- Green pixels: labels
- Grey pixels: both model and labels agree
- Pink squares: significant differences between both

<a href=""><img src="" /></a>

Filter training DataSet by selecting then removing unwanted outliers
Manually Filter training DataSet by selecting unwanted outliers
neo compare --cover gl/outliers.cover.csv --mode side --images gl/images gl/compare --out gl/compare_outliers

It would take about an hour to a human, to do so.
Here a possible result:
wget -O gl/remove.cover.csv

Then filter our dataset ro remove the unwanted outliers.
neo subset --dir gl/images --cover gl/cover.csv --out gl/filter/images
neo subset --dir gl/labels --cover gl/cover.csv --out gl/filter/labels
neo subset --dir gl/filter/images --delete --cover gl/remove.cover.csv