Document data sampling strategy

docs/_toc.yml

@@ -22,6 +22,8 @@ parts:
       file: data_datacube
     - title: Benchmark dataset labels
       file: data_labels
+    - title: Data sampling strategy
+      file: data_sampling
 - caption: Running the model
   chapters:
     - title: Run over a region

docs/data_sampling.md

@@ -0,0 +1,47 @@
+# 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

docs/specification.md

@@ -21,7 +21,7 @@ We also generated embeddings for all trainning data, which can be found on Sourc
 
 ## Model Architecture
 
-Clat is a Unet, with a modified ViT encoder down to embeddings, and a decoder to reconstruct the masked parts of the original image. The loss function is the MSE between the original image and the reconstructed image.
+Clay is a MAE, with a modified ViT encoder down to embeddings, and a decoder to reconstruct the masked parts of the original image. The loss function is the MSE between the original image and the reconstructed image.
 
 For details, check the source code [here](https://github.com/Clay-foundation/model/blob/v0.0.1/src/model_clay.py).