stactools-esa-cci-lc


Namestactools-esa-cci-lc JSON
Version 0.1.0 PyPI version JSON
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home_pagehttps://github.com/stactools-packages/esa-cci-lc
Summarystactools package for ESA''s Climate Change Initiative (CCI) Land Cover (LC) product
upload_time2022-12-19 20:26:25
maintainer
docs_urlNone
authorMatthias Mohr
requires_python
license
keywords stactools esa cci land cover climate change stac
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            # stactools-esa-cci-lc

[![PyPI](https://img.shields.io/pypi/v/stactools-esa-cci-lc)](https://pypi.org/project/stactools-esa-cci-lc/)

- Name: esa-cci-lc
- Package: `stactools.esa_cci_lc`
- [stactools-esa-cci-lc on PyPI](https://pypi.org/project/stactools-esa-cci-lc/)
- Owner: @m-mohr
- Dataset homepage:
  - <https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover>
- STAC extensions used:
  - [classification](https://github.com/stac-extensions/classification/)
  - [datacube](https://github.com/stac-extensions/datacube/)
  - [processing](https://github.com/stac-extensions/processing/)
  - [proj](https://github.com/stac-extensions/projection/)
  - [raster](https://github.com/stac-extensions/raster/)
  - [version](https://github.com/stac-extensions/version/)
- Extra fields:
  - None

A stactools package for ESA's Climate Change Initiative (CCI) Land Cover (LC)
product.

This dataset provides global maps describing the land surface classes,
which have been defined using the United Nations Food and Agriculture
Organization's (UN FAO) Land Cover Classification System (LCCS).
In addition to the land cover (LC) maps, four quality flags are produced to
document the reliability of the classification and change detection.
In order to ensure continuity, these land cover maps are consistent with the
series of global annual LC maps from the 1990s to 2015 produced by the
European Space Agency (ESA) Climate Change Initiative (CCI).

This package can generate STAC files from netCDF files and that either link to
the original netCDF files or to Cloud-Optimized GeoTiff (COG) files.

## STAC Examples

- [Collection](examples/collection.json)
- [Item](examples/item.json)
- [Browse the example in human-readable form](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/stactools-packages/esa-cci-lc/main/examples/collection.json)

## Installation

```shell
pip install stactools-esa-cci-lc
```

## Command-line Usage

Use `stac esa-cci-lc --help` to see all subcommands and options.

### Collection

Create a collection:

```shell
stac esa-cci-lc create-collection collection.json
```

Get information about all options for collection creation:

```shell
stac esa-cci-lc create-collection --help
```

### Item

Create an item with netCDF and COG assets:

```shell
stac esa-cci-lc create-item /path/to/source/file.nc item.json --collection collection.json
```

Create an item with only COG assets:

```shell
stac esa-cci-lc create-item /path/to/source/file.nc item.json --collection collection.json --nonetcdf TRUE
```

Get information about all options for item creation:

```shell
stac esa-cci-lc create-item --help
```

## Contributing

We use [pre-commit](https://pre-commit.com/) to check any changes.
To set up your development environment:

```shell
pip install -e .
pip install -r requirements-dev.txt
pre-commit install
```

To check all files:

```shell
pre-commit run --all-files
```

To run the tests:

```shell
pytest -vv
```

### Tests

Due to the large file sizes, the data files for the tests are missing in this repository.
To run the tests, please download the files for 1992, 2015, 2016 and 2020 from the
[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=form).
You can download them all at once in a ZIP or gzipped TAR file and then extract the `.nc` files
to the `tests/data-files/external` folder.

The CI doesn't run any tests for item generation due to the missing data files.
Please ensure you've tested the changes against the data files locally!

Also, due to the long processing time of the tests with COGs conversion (in total 3-4 hours on 
my local machine), the tests by default run without COG processing (i.e. `--nocog` is enabled).
To run the tests with COG generation, please run `pytest -vv --withcog`.

Raw data

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    "description": "# stactools-esa-cci-lc\n\n[![PyPI](https://img.shields.io/pypi/v/stactools-esa-cci-lc)](https://pypi.org/project/stactools-esa-cci-lc/)\n\n- Name: esa-cci-lc\n- Package: `stactools.esa_cci_lc`\n- [stactools-esa-cci-lc on PyPI](https://pypi.org/project/stactools-esa-cci-lc/)\n- Owner: @m-mohr\n- Dataset homepage:\n  - <https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover>\n- STAC extensions used:\n  - [classification](https://github.com/stac-extensions/classification/)\n  - [datacube](https://github.com/stac-extensions/datacube/)\n  - [processing](https://github.com/stac-extensions/processing/)\n  - [proj](https://github.com/stac-extensions/projection/)\n  - [raster](https://github.com/stac-extensions/raster/)\n  - [version](https://github.com/stac-extensions/version/)\n- Extra fields:\n  - None\n\nA stactools package for ESA's Climate Change Initiative (CCI) Land Cover (LC)\nproduct.\n\nThis dataset provides global maps describing the land surface classes,\nwhich have been defined using the United Nations Food and Agriculture\nOrganization's (UN FAO) Land Cover Classification System (LCCS).\nIn addition to the land cover (LC) maps, four quality flags are produced to\ndocument the reliability of the classification and change detection.\nIn order to ensure continuity, these land cover maps are consistent with the\nseries of global annual LC maps from the 1990s to 2015 produced by the\nEuropean Space Agency (ESA) Climate Change Initiative (CCI).\n\nThis package can generate STAC files from netCDF files and that either link to\nthe original netCDF files or to Cloud-Optimized GeoTiff (COG) files.\n\n## STAC Examples\n\n- [Collection](examples/collection.json)\n- [Item](examples/item.json)\n- [Browse the example in human-readable form](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/stactools-packages/esa-cci-lc/main/examples/collection.json)\n\n## Installation\n\n```shell\npip install stactools-esa-cci-lc\n```\n\n## Command-line Usage\n\nUse `stac esa-cci-lc --help` to see all subcommands and options.\n\n### Collection\n\nCreate a collection:\n\n```shell\nstac esa-cci-lc create-collection collection.json\n```\n\nGet information about all options for collection creation:\n\n```shell\nstac esa-cci-lc create-collection --help\n```\n\n### Item\n\nCreate an item with netCDF and COG assets:\n\n```shell\nstac esa-cci-lc create-item /path/to/source/file.nc item.json --collection collection.json\n```\n\nCreate an item with only COG assets:\n\n```shell\nstac esa-cci-lc create-item /path/to/source/file.nc item.json --collection collection.json --nonetcdf TRUE\n```\n\nGet information about all options for item creation:\n\n```shell\nstac esa-cci-lc create-item --help\n```\n\n## Contributing\n\nWe use [pre-commit](https://pre-commit.com/) to check any changes.\nTo set up your development environment:\n\n```shell\npip install -e .\npip install -r requirements-dev.txt\npre-commit install\n```\n\nTo check all files:\n\n```shell\npre-commit run --all-files\n```\n\nTo run the tests:\n\n```shell\npytest -vv\n```\n\n### Tests\n\nDue to the large file sizes, the data files for the tests are missing in this repository.\nTo run the tests, please download the files for 1992, 2015, 2016 and 2020 from the\n[Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=form).\nYou can download them all at once in a ZIP or gzipped TAR file and then extract the `.nc` files\nto the `tests/data-files/external` folder.\n\nThe CI doesn't run any tests for item generation due to the missing data files.\nPlease ensure you've tested the changes against the data files locally!\n\nAlso, due to the long processing time of the tests with COGs conversion (in total 3-4 hours on \nmy local machine), the tests by default run without COG processing (i.e. `--nocog` is enabled).\nTo run the tests with COG generation, please run `pytest -vv --withcog`.\n",
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Matthias Mohr
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