How to run clay over custom AOIs#

This section shows in a few simple steps how the clay model can be run for custom AOIs and over custom date ranges.

Prepare folder strucutre for data#

# Move into the model repository
cd /path/to/repository/model/

# Ensure data sub-directories exist
mkdir data/mgrs
mkdir data/chips
mkdir data/embeddings

Download global list of MGRS tiles#

The first step is to download a list of globally available MGRS tiles. A full list of MGRS tiles has been created as part of the landcover based sampling strategy. The file is sourced from a complete MGRS tile list, and then itersected with the WorldCover landcover layer, outputting the mgrs_full.fgb file that is used below.

wget -O data/mgrs/mgrs_full.fgb

Create a Geopandas dataframe with MGRS tiles over the AOI#

This example uses a bounding box over the area around Puri, India, to filter the global list of MGRS tiles. The intersected MGRS tiles are then stored into a new dataset with the reduced list. The reduced list will be used by the script for creating imagery chips.

import geopandas as gpd
import pandas as pd
from shapely import box

mgrs = gpd.read_file("data/mgrs/mgrs_full.fgb")
print(f"Loaded {len(mgrs)} MGRS grid cells.")

aoi = gpd.GeoDataFrame(
    pd.DataFrame(["Puri"], columns=["Region"]),
    geometry=[box(85.0503, 19.4949, 86.1042, 20.5642)],
mgrs_aoi = mgrs.overlay(aoi)

# Rename the name column to use lowercase letters for the datacube script to
# pick upthe MGRS tile name.
mgrs_aoi = mgrs_aoi.rename(columns={"Name": "name"})

print(f"Found {len(mgrs_aoi)} matching MGRS tiles over the AOI.")


Use the script to download imagery#

This will select the MGRS tiles that intersect with your AOI. The processing will then happen for each of the MGRS tiles. This will most likely provide slightly more data than the AOI itself, as the whole tile data will be downloaded for each matched MGRS tile.

Each run of the datacube script will take an index as input, which is the index of the MGRS tile within the input file. This is why we need to download the data in a loop.

A list of date ranges can be specified. The script will look for the least cloudy Sentinel-2 scene for each date range and match Sentinel-1 dates near the identified Sentinel-2 dates.

The output folder can be specified as a local folder or a bucket can be specified if you want to upload the data to S3.

Note that for the script to run, a Microsoft Planetary Computer token needs to be set up. Consult the Planetary Computer SDK documentation on how to set up the token.

By default, the datacube script will download all the data available for each MGRS tile it processes, so the output might include imagery chips that are outside of the AOI specified.

To speed up processing in the example below, we use the subset argument to reduce each MGRS tile to a small pixel window. When subsetting, the script will only download a fraction of each MGRS tile. This will lead to discontinous datasets and should not be used in a real use case. Remove the subset argument when using the script for a real world application, where all the data should be downloaded for each MGRS tile.

for i in {0..5}; do

python scripts/pipeline/ \
    --sample data/mgrs/mgrs_aoi.fgb \
    --localpath data/chips  \
    --index $i \
    --dateranges 2020-01-01/2020-04-01,2021-06-01/2021-09-15 \
    --subset 1500,1500,2524,2524;


Create the embeddings for each training chip#

The checkpoints can be accessed directly from Hugging Face at

The following command will run the model to create the embeddings and automatically download and cache the model weights.

wandb disabled
python predict \
    --ckpt_path= \
    --trainer.precision=16-mixed \
    --data.data_dir=/home/tam/Desktop/aoitiles \
    --data.batch_size=2 \