Metadata-Version: 2.1
Name: bigearthnet-encoder
Version: 0.2.1
Summary: A flexible BigEarthNet encoder that allows one to quickly convert BigEarthNet to a DL-optimization data format.
Home-page: https://github.com/kai-tub/bigearthnet_encoder/
License: MIT
Author: Kai Norman Clasen
Author-email: k.clasen@protonmail.com
Requires-Python: >=3.8,<3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: bigearthnet-common (>=2,<3)
Requires-Dist: bigearthnet-patch-interface (>=0.1,<0.2)
Requires-Dist: fastcore (>=1.3.27,<2.0.0)
Requires-Dist: lmdb (>=1.3.0,<2.0.0)
Requires-Dist: numpy (>=1.19,<2.0)
Requires-Dist: pandas (>=1.3.5,<2.0.0)
Requires-Dist: pydantic (>=1.9.0,<2.0.0)
Requires-Dist: rasterio (>=1.2.10,<2.0.0)
Requires-Dist: rich (>=10,<14)
Requires-Dist: rich-click[typer] (>=1.3.2,<2.0.0)
Project-URL: Documentation, https://github.com/kai-tub.github.io/bigearthnet_encoder
Project-URL: Repository, https://github.com/kai-tub/bigearthnet_encoder/
Description-Content-Type: text/markdown

# BigEarthNet Encoder

[![Tests](https://img.shields.io/github/workflow/status/kai-tub/bigearthnet_encoder/CI?color=dark-green&label=%20Tests)](https://github.com/kai-tub/bigearthnet_encoder/actions/workflows/main.yml)
[![License](https://img.shields.io/pypi/l/bigearthnet_encoder?color=dark-green)](https://github.com/kai-tub/bigearthnet_encoder/blob/main/LICENSE)
[![PyPI version](https://badge.fury.io/py/bigearthnet-encoder.svg)](https://pypi.org/project/bigearthnet-encoder/)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/bigearthnet-encoder?color=dark-green)](https://anaconda.org/conda-forge/bigearthnet-encoder)
[![Auto Release](https://img.shields.io/badge/release-auto.svg?colorA=888888&colorB=9B065A&label=auto&logo=data:image/png;base64,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)](https://github.com/intuit/auto)

The goal of the BigEarthNet Encoder library is to quickly transform the original BigEarthNet archive into a deep-learning optimized format.
The long-term goal is to support multiple output formats.

To simplify the process of working with BigEarthNet, each patch is first converted to a [BigEarthNet-Patch-Interface](https://docs.kai-tub.tech/bigearthnet_patch_interface/).
This interface will guarantee that the data is complete and valid before moving on to creating the desired format.
The patch data is internally stored as a NumPy array to keep the data in a framework-agnostic format.

The library should provide all the necessary functionality via a CLI to allow for quick conversion without requiring an in-depth understanding of the library.

As of now, the only supported target format is the [LMDB](lmdb) archive format.

