Initial release of LUCAS-MEGA

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+resources/*.png filter=lfs diff=lfs merge=lfs -text

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LICENSE

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+MIT License
+
+Copyright (c) 2025 Kuangdai
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# LUCAS-MEGA
+
+**LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems**
+
+[Manuscript](manuscript.pdf)
+
+---
+
+# Introduction
+
+LUCAS-MEGA is a large-scale multimodal dataset for soil-environment systems, built by fusing heterogeneous European soil
+and environmental datasets with the LUCAS soil survey as the backbone.
+
+The released dataset contains:
+
+- 72,552 soil samples
+- 1,017 fused soil and environmental features
+- 68 integrated ESDAC source datasets
+- Numerical, categorical, textual, vector-valued, and visual data
+
+LUCAS-MEGA is designed for representation learning in soil science. It provides a unified sample-feature space where
+models can learn relationships across soil, climate, terrain, land-use, hydrological, and environmental variables.
+
+The dataset follows the **MEGA** principles:
+
+- (**M**)ultimodal: scalar, vector-valued, categorical, textual, and visual features.
+- (**E**)nd-to-end machine learning-ready: standardized units, harmonized formats, unified schema, and machine-readable
+  metadata.
+- (**G**)reat quality: corrected unit issues, invalid values, codebook mismatches, missing-value conventions, and
+  cross-dataset inconsistencies.
+- (**A**)ccessible: released with table and dictionary formats, metadata, assets, visualization tools, and API-oriented
+  resources.
+
+The final LUCAS-MEGA dataset is the **fused representation** and is intended for most users, including model training,
+soil-environment analysis, and downstream applications.
+
+The **standardized representation** is an intermediate layer between raw ESDAC datasets and the final fused dataset. It
+contains cleaned and normalized individual source datasets before fusion, and is mainly useful for inspection,
+debugging, and extension.
+
+---
+
+# Download
+
+## Download LUCAS-MEGA
+
+For users who only need the final fused dataset (16 GB):
+
+```bash
+git clone https://huggingface.co/datasets/earthroverprogram/lucas-mega
+cd lucas-mega
+git lfs pull --include="datasets/fusion" --exclude="datasets/esdac,src/fuse_esdac/large_inputs"
+```
+
+The fusion dataset is the released LUCAS-MEGA dataset. It integrates standardized source datasets into a unified
+sample-feature representation. Each sample is a soil observation, and each feature is a fused soil or environmental
+variable. Features may be scalar values, categorical labels, text fields, vector-valued measurements, images, or links
+to dense asset files.
+
+### Released Files
+
+| File                        | Description                                                                                                                                               |
+|-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `data_table.csv`            | Main flat table. Each row is a soil sample; each column is a fused feature.                                                                               |
+| `data_dict.json`            | Hierarchical sample-level JSON with detailed feature metadata.                                                                                            |
+| `data_dict.pkl`             | Same content as `data_dict.json`, stored as Python pickle for faster loading.                                                                             |
+| `meta_column_complete.json` | Complete metadata for fused features, including units, provenance, modality, and fusion information where available.                                      |
+| `meta_column_names.json`    | List of fused feature names.                                                                                                                              |
+| `meta_fused_datasets.csv`   | Metadata and provenance of source datasets used in fusion.                                                                                                |
+| `gadm_tree_europe.pkl`      | GADM administrative hierarchy attached to samples for geographic reasoning and regional lookup.                                                           |
+| `assets/`                   | Dense feature data, including hydraulic conductivity curves, water retention curves, particle size distribution spectra, and site images where available. |
+
+---
+
+## Download the Standardized Representation
+
+For users interested in the intermediate standardized data (70 GB):
+
+```bash
+git lfs pull --include="datasets/esdac" --exclude="datasets/fusion,src/fuse_esdac/large_inputs"
+```
+
+The standardized representation contains cleaned and normalized individual source datasets. These datasets have been
+converted into common formats but are not yet fused into the final LUCAS-MEGA sample-feature table.
+
+It includes:
+
+- standardized tabular files for sample-structured datasets;
+- standardized geospatial files for map-structured datasets;
+- linked asset files for high-dimensional objects such as curves and spectra.
+
+This layer is useful for inspecting source datasets, understanding preprocessing, developing new fusion rules, debugging
+the pipeline, or extending LUCAS-MEGA.
+
+Use `viewer.py` to visualize the standardized data and metadata:
+
+```bash
+python viewer.py
+```
+
+<img src="resources/preview.png" alt="preview" style="width:50%;">
+
+---
+
+# For Developers
+
+This repository also contains the codebase used to generate LUCAS-MEGA from raw ESDAC datasets.
+
+The pipeline includes:
+
+- **Source dataset downloading**: requesting and downloading source datasets from ESDAC. This is not handled by this
+  repository because the original datasets are subject to ESDAC access and license terms.
+- **Data standardization**: converting heterogeneous datasets into a common representation, including format conversion,
+  unit normalization, coordinate handling, codebook harmonization, invalid-value correction, and metadata organization.
+- **Data fusion**: aligning standardized datasets into the released LUCAS-MEGA sample-feature schema, with provenance
+  and metadata attached to fused features.
+
+Example:
+
+- Source download:  
+  https://esdac.jrc.ec.europa.eu/content/lucas-2009-topsoil-data
+
+- Standardization code:  
+  [src/esdac/lucas-2009-topsoil-data/process.py](src/esdac/lucas-2009-topsoil-data/process.py)
+
+- Fusion specification:  
+  [src/esdac/lucas-2009-topsoil-data/fuse_schema.json](src/esdac/lucas-2009-topsoil-data/fuse_schema.json)
+
+## Reproducing and Extending the Dataset
+
+Due to license agreements, we cannot redistribute the original ESDAC source datasets.
+
+To fully reproduce the released dataset, users must request and download the source datasets from ESDAC separately and
+accept the corresponding license terms.
+
+The complete dataset list is available at:
+
+[src/esdac/status.json](src/esdac/status.json)
+
+To reproduce the released version, download datasets with `status = PROCESSED`. At release time, this includes 95 ESDAC
+datasets.
+
+Full reproduction is time-consuming because source datasets must be requested individually, and access conditions may
+vary by dataset.
+
+If you intend to reproduce the full pipeline or extend LUCAS-MEGA with additional datasets, please contact us. We can
+provide practical guidance that is not included here due to source-data licensing restrictions.