LUCAS-MEGA

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems

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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:

• 72,000+ soil samples
• 1,000+ fused soil and environmental features
• 68 integrated ESDAC source datasets
• Numerical (scalar- and vector-valued), categorical, textual, 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.

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Download

Download LUCAS-MEGA

For users who only need the final fused dataset (16 GB):

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 numerical 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.

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Download the Standardized Representation

For users interested in the intermediate standardized data (70 GB):

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 tabular transformation.

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:

python viewer.py

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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

• Fusion specification:
  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

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.