odc-loader
Tooling for constructing xarray objects from parsed metadata
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; rasterio may require system libraries (GDAL) depending on your environment.
- 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 · maintenance · safety
permissive license (permissive) — Permissive license treatment means you can use, modify, and redistribute this package with minimal legal restrictions in most commercial and open-source contexts.
last release 2026-01-14 (212 days) · last repo commit 2026-07-30 · 7 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 96,399 downloads/mo, #13,214 on PyPI
Alternatives
Verify before relying
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()- 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.
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 on this page. Verify before relying on it.
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
odc-loader on PyPI
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.
Requires Python 3.10 or later; rasterio may require system libraries (GDAL) depending on your environment.
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()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 | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesodc-georasteriodasknumpyxarraytyping-extensions |
| Maintenance | Actively maintained 212 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 96,399 / month, #13,214 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: GISTopic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: odc_loader-0.6.4-py3-none-any.whl
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See also odc-stac · odc-geo · rasterix · xarray-spatial · xarray · rioxarray · xproj · spatial_image · multiscale-spatial-image · flox