Document data sampling strategy

docs/data_sampling.md

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+# Data sampling strategy
+
+To create a balanced dataset for model training, we used a sampling strategy
+based on land cover classes from the [ESA WorldCover](https://esa-worldcover.org/)
+layer.
+
+Our unit of analysis for sampling was the MGRS tile, the global tiling scheme
+that is used for distributing Sentinel-2 imagery. For each MGRS tile, we
+computed landcover statistics for all the classes in the WorldCover layer. To
+speed up processing, we used the third level overview in the WorldCover layer,
+which has a spatial resolution of 80 meters.
+
+The goal of the landcover sampling was to ensure coverage of each class at
+a reasonable level. For each class, we selected a number of random MGRS tiles
+out of the subset of MGRS tiles with the highest fraction of that class present.
+
+As an example, for "Wetlands" we selected 50 random ones out of the MGRS tiles
+with the highest wetland fraction globally. For the Built-up class on the other
+hand we selected the 400 most urban MGRS tiles.
+
+In addition to the landcover classes, we also added diversity by selecting 500
+tiles out of the 3000 tiles with the highest count of land cover classes present
+in the tile.
+
+The following table summarizes the selection criteria for each class.
+
+| Class | Nr of Tiles | From highest |
+|---|---|---|
+Diversity | 500 | 3000
+Built-up | 400 | 400
+Herbaceous wetland | 50 | 500
+Mangroves | 50 | 500
+Moss and lichen | 50 | 500
+Cropland | 100 | 500
+Tree cover | 100 | 500
+Shrubland | 50 | 500
+Grassland | 50 | 500
+Bare / sparse vegetation | 50 | 500
+Snow and Ice | 50 | 500
+Permanent water bodies | 100 | 1000
+
+After selecting MGRS tiles for each of these criteria, we removed duplicates.
+This resulted in a sample of 1517 MGRS tiles total in our sample.
+
+The resulting sample file can be downloaded from the following link
+
+https://clay-mgrs-samples.s3.amazonaws.com/mgrs_sample.fgb