---
id: odc-loader
version: "0.6.4"
license: unclear
license_treatment: permissive
maintenance: active
---

# odc-loader — Tooling for constructing xarray objects from parsed metadata

License: permissive · Maintenance: active · Downloads: 96.4K/mo

## What it is and what it does

odc-loader is a Python library that bridges parsed geospatial metadata and xarray, a popular multi-dimensional array library. It provides tooling to construct xarray Dataset and DataArray objects from metadata, typically used in geospatial and Earth observation workflows where raster data needs to be loaded and organized into labeled, multi-dimensional structures.

The package integrates with dask for lazy loading, rasterio for raster I/O, and odc-geo for geospatial operations. It targets developers working with gridded geospatial data who need programmatic control over how metadata translates into array structures. The library supports Python 3.10 through 3.13 and carries a permissive license, making it suitable for both research and production use.

Use it for:

- Load satellite or aerial imagery metadata and construct xarray datasets for analysis in Jupyter notebooks.
- Build lazy-loaded dask-backed arrays from geospatial metadata for memory-efficient processing of large raster collections.
- Integrate Earth observation data pipelines where metadata parsing and array construction need to be decoupled.
- Construct labeled multi-dimensional arrays from geospatial metadata for time-series or multi-band raster analysis.
- Automate xarray object creation in geospatial data processing workflows that consume parsed metadata.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Constructs xarray objects from parsed geospatial metadata, enabling programmatic assembly of multi-dimensional arrays for raster data workflows.

Yes, if you are working with geospatial raster data and need to programmatically construct xarray objects from metadata. The package has low install friction, active maintenance, no known vulnerabilities, and a permissive license. The six dependencies are standard in geospatial Python (rasterio, dask, xarray, numpy). Install it if your workflow involves metadata-driven array construction; skip it if you only need basic raster I/O without xarray integration.

## Install

pip install odc-loader
uv add odc-loader
poetry add odc-loader

## Installing odc-loader

Before you install: Low friction: pure Python wheel with six runtime dependencies (odc-geo, rasterio, dask, numpy, xarray, typing-extensions). Repository is active with recent commits; last release was 212 days ago, indicating regular maintenance.

License in practice: Permissive license treatment means you can use, modify, and redistribute this package with minimal legal restrictions in most commercial and open-source contexts.

Quickstart:

pip install odc-loader

from odc.loader import construct_loader
import xarray as xr

# Construct xarray dataset from parsed metadata
loader = construct_loader(metadata)
dataset = loader.load()

Requires Python 3.10 or later; rasterio may require system libraries (GDAL) depending on your environment.

Verify before relying:

- Whether the package is actively maintained beyond the last commit date or if 212 days represents a stable release cycle.
- Specific use-case scenarios beyond generic xarray construction from metadata.
- Performance characteristics when working with large datasets via dask integration.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 96.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

xarray from metadata, geospatial raster loading, construct xarray datasets, metadata to array conversion, raster data assembly, odc geospatial tools, dask raster loading, geospatial, raster-data, xarray

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