SatChip

A package for satellite image AI data prep. This package "chips" data labels and satellite imagery into 264x264 image arrays following the TerraMind extension of the MajorTom specification.

Using

SatChip relies on a two-step process; chip your label train data inputs, then create corresponding chips for different remote sensing data sources.

Step 1: Chip labels

The chiplabel CLI tool takes a GDAL-compatible image, a collection date, and an optional output directory as input using the following format:

chiplabel PATH/TO/LABELS.tif DATE(UTC FORMAT) --outdir OUTPUT_DIR

For example:

chiplabel LA_damage_20250113_v0.tif 2024-01-01T01:01:01 --outdir chips

This will produce an output zipped Zarr store label dataset with the name {LABELS}.zarr.zip in the specified output directory (--outdir). This file will be the input to the remote sensing data chipping step.

For more information on usage see chiplabel --help

Step 2: Chip remote sensing data

The chipdata CLI tool takes a label zipped Zarr store, a dataset name, and an optional output directory as input using the following format:

chipdata PATH/TO/LABELS.zarr.zip DATASET --outdir OUTPUT_DIR

For example:

chipdata LA_damage_20250113_v0.zarr.zip S2L2A --outdir chips

Similarly to step 1, this will produce an output zipped Zarr store that contains chipped data for your chosen dataset with the name {LABELS}_{DATASET}.zarr.zip.

Currently support datasets include:

• S2L2A: Sentinel-2 L2A data sourced from the Sentinel-2 AWS Open Data Archive
• S1RTC: Sentinel-1 Radiometric Terrain Corrected (RTC) data created using ASF's HyP3 on-demand platform

License

SatChip is licensed under the BSD-3-Clause open source license. See the LICENSE file for more details.

Contributing

Contributions to the SatChip are welcome! If you would like to contribute, please submit a pull request on the GitHub repository.