| --- |
| 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 |
|
|
| ```python |
| import pandas as pd |
| |
| df = pd.read_parquet("mining_datapoints.parquet") |
| print(df.columns) |
| ``` |
|
|
| For geospatial workflows, convert `x`/`y` into geometries using `geopandas`: |
|
|
| ```python |
| 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. |
|
|
