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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/terrafloww/rasteret/llms.txt

Use this file to discover all available pages before exploring further.

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Installation

Rasteret requires Python 3.12+ and is available on PyPI.

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Basic Installation

The basic installation includes all dependencies needed for building Collections from STAC APIs and reading data as NumPy arrays or GeoPandas DataFrames.

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Optional Extras

Rasteret provides optional extras for specific workflows. Install only what you need:

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xarray Support

For analysis workflows using xarray: 
uv pip install "rasteret[xarray]"
Includes: xarray (≥2024.1.0) Use when: You want to use collection.get_xarray() for analysis, visualization, or geospatial computations.

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TorchGeo Integration

For ML training with PyTorch and TorchGeo: 
uv pip install "rasteret[torchgeo]"
Includes: torchgeo (≥0.9.0) Use when: You want to use collection.to_torchgeo_dataset() for training with PyTorch DataLoader, TorchGeo samplers, and transforms.

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Cloud Provider Authentication

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AWS (S3)

For requester-pays buckets (Landsat, NAIP, etc.): 
uv pip install "rasteret[aws]"
Includes: boto3 (≥1.34.0) Use when: Working with AWS S3 requester-pays datasets like:
  • Landsat Collection 2 on Earth Search
  • NAIP imagery
  • Any S3-hosted dataset requiring AWS credentials
You must configure AWS credentials before using requester-pays data:
aws configure
Or set environment variables:
export AWS_ACCESS_KEY_ID=your_key
export AWS_SECRET_ACCESS_KEY=your_secret
export AWS_REGION=us-west-2

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Azure (Planetary Computer)

For Microsoft Planetary Computer signed URLs: 
uv pip install "rasteret[azure]"
Includes: planetary-computer (≥1.0.0), requests (≥2.31.0) Use when: Accessing datasets from Microsoft Planetary Computer:
  • Sentinel-2 on Planetary Computer
  • ALOS DEM
  • NASADEM
  • ESA WorldCover
  • USDA Cropland Data Layer
Planetary Computer requires signing up for a free API key at planetarycomputer.microsoft.com.

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NASA Earthdata

For NASA LP DAAC and Earthdata-hosted collections: 
uv pip install "rasteret[earthdata]"
Includes: requests (≥2.31.0) Use when: Accessing NASA datasets requiring Earthdata credentials.
Configure credentials in ~/.netrc:
machine urs.earthdata.nasa.gov
login YOUR_USERNAME
password YOUR_PASSWORD

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Combining Extras

Install multiple extras by combining them with commas: 
# ML workflow with AWS access
uv pip install "rasteret[torchgeo,aws]"

# Analysis workflow with xarray and Azure
uv pip install "rasteret[xarray,azure]"

# Complete installation (not recommended unless you need everything)
uv pip install "rasteret[xarray,torchgeo,aws,azure,earthdata]"

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Verify Installation

Check that Rasteret is installed correctly: 
import rasteret

print(rasteret.__version__)
# Output: 0.x.x
List available datasets: 
rasteret datasets list
You should see a table of built-in datasets with their IDs, names, coverage, and authentication requirements.

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Python Version Requirements

Rasteret VersionPython Version
0.x.x (current)≥3.12
Rasteret requires Python 3.12 or newer. If you’re using an older Python version, you must upgrade:
# Using uv
uv python install 3.12

# Using pyenv
pyenv install 3.12
pyenv local 3.12

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Core Dependencies

The base installation includes:
  • numpy (≥1.24.0) — Array operations
  • pyarrow (≥14.0.1) — Parquet reading/writing
  • pystac-client (≥0.7.5) — STAC API client
  • obstore (≥0.8.0) — Object storage I/O
  • geopandas (≥0.13) — Geospatial DataFrames
  • rasterio (≥1.4.3, <1.5.0) — GeoTIFF metadata
  • pyproj (≥3.6.1) — CRS transformations
  • affine (≥2.4.0) — Affine transformations
  • imagecodecs (≥2023.9.18) — Image compression codecs
These are installed automatically and require no extra configuration.

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System Requirements

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Operating Systems

✅ Linux (primary development platform)
✅ macOS (Intel and Apple Silicon)
✅ Windows (via WSL2 recommended)

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Hardware

Minimum:
  • 4 GB RAM
  • 2 CPU cores
  • 1 GB disk space for workspace cache
Recommended:
  • 16+ GB RAM for large collections
  • 4+ CPU cores for parallel COG header parsing
  • SSD for workspace cache
Collections are stored as Parquet files in ~/rasteret_workspace/ by default. A typical collection with 1000 scenes uses 10-50 MB of disk space.

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Troubleshooting

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ImportError: No module named ‘rasteret’

Ensure you’re using Python 3.12+: 
python --version
# Should show Python 3.12.x or higher

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GDAL/Rasterio Issues

Rasterio is only used for metadata parsing, not pixel I/O. If you encounter GDAL errors:
  1. Update rasterio: 
    uv pip install --upgrade rasterio
  2. If problems persist, Rasteret can work without rasterio for some workflows (using obstore directly).

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AWS Credentials Not Found

For requester-pays datasets: 
# Configure AWS CLI
aws configure

# Or set environment variables
export AWS_ACCESS_KEY_ID=...
export AWS_SECRET_ACCESS_KEY=...
export AWS_DEFAULT_REGION=us-west-2

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Planetary Computer Authentication

Sign up for a free API key and configure: 
import planetary_computer as pc
pc.set_subscription_key("YOUR_KEY")

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Next Steps

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