miningexploration_dataset_card.md

metadata

language:
  - en
license: other
tags:
  - remote-sensing
  - earth-observation
  - mineral-exploration
datasets:
  - Thomaschtl/miningexploration

Mining Exploration Datapoints

Dataset Summary

This dataset contains per-pixel feature vectors engineered for mineral exploration around the Bou Azzer district (Morocco). Each sample aggregates multi-resolution satellite observations into a single record with georeferenced coordinates and a rich spectral/radar feature stack derived from Sentinel-1, Sentinel-2, ASTER, Landsat 8, and a Digital Elevation Model (DEM).

The physical cubes (Zarr) were used to generate this dataset.
Due to size constraints, only derived parquet tables are hosted here.

Field	Description
x, y	Projected coordinates in EPSG:32629 (UTM Zone 29N) defining the pixel centroid.
Sentinel-2 fields	VNIR/SWIR reflectances (s2_b02, s2_b03, s2_b04, s2_b08, s2_b11, s2_b12) plus cloud/validity masks (s2_vnir_mask, s2_swir_mask).
Sentinel-1 fields	Radar backscatter (s1_vv, s1_vh), ratio (s1_vv_div_vh), and validity mask (s1_mask).
ASTER fields	VNIR/SWIR reflectances (aster_vnir_b01, aster_swir_b04‒aster_swir_b09) with corresponding masks.
Landsat fields	Multispectral reflectances (landsat_b02‒landsat_b07) and validity mask (landsat_mask).
DEM fields	Elevation (dem_elevation) and validity mask (dem_mask).
label	Optional target label for downstream tasks (currently unset).

Data structure

Two export formats are available:

• mining_datapoints.parquet — columnar, compressed Parquet table (recommended for analytics and ML pipelines).
• mining_datapoints.csv — wide CSV table for compatibility with spreadsheet tools (≈1.6 GB).

Both files share the same schema described above.

Source data & processing

1. Sentinel-2 L2A mosaics (10 m) and Sentinel-1 median composites (10 m) are aligned on a 10 m grid and validated with sensor-specific masks.
2. ASTER VNIR/SWIR, Landsat 8 OLI, and DEM inputs (30 m) are resampled and co-registered to the same spatial footprint.
3. The 10 m cube is downscaled and merged with the 30 m cube band-wise.
4. Each pixel is transformed into a MiningDataPoint record with flattened spectral and ancillary features.

Processing scripts live in the cube/ and datapoint/ modules of the Mirage Metrics repository.

Usage

import pandas as pd

df = pd.read_parquet("mining_datapoints.parquet")
print(df.columns)

For geospatial workflows, convert x/y into geometries using geopandas:

import geopandas as gpd
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.x, df.y), crs="EPSG:32629")

Recommended citation

Please cite the Mirage Metrics project or provide attribution to the dataset maintainers when reusing these samples. Replace this section with a full citation once the project has a formal reference.

License

The license is currently marked as other. Update this section with the appropriate license text before publishing.