Global ML-ready dataset for mining areas in satellite images

This dataset is a global resource for machine learning applications in mining area detection and semantic segmentation on satellite imagery. It contains Sentinel-2 satellite images and corresponding mining area masks + bounding boxes for 1,210 sites worldwide. Ground-truth masks are derived from Maus et al. (2022) and Tang et al. (2023), and validated through manual verification to ensure accurate alignment with Sentinel-2 imagery from specific timestamps. 

The dataset includes three mask variants:

Masks exclusively from Maus et al. (n=1,090)

Masks exclusively from Tang et al. (n=817)

A preferred mask selected from either Maus or Tang based on alignment quality determined during manual review (n=1,210).

Each tile corresponds to a 2048x2048 pixel Sentinel-2 image, with metadata on mine type (surface, placer, underground, brine & evaporation) and scale (artisanal, industrial). For convenience, the preferred mask dataset is already split into training (75%), validation (15%), and test (10%) sets. 

Furthermore, dataset quality was validated by re-validating test set tiles manually and correcting any mismatches between mining polygons and visually observed true mining area in the images, resulting in the following estimated quality metrics: 

Combined
Maus
Tang

Accuracy
99.78
99.74
99.83

Precision
99.22
99.20
99.24

Recall
95.71
96.34
95.10

Note that the dataset does not contain the Sentinel-2 images themselves but contains a reference to specific Sentinel-2 images. Thus, for any ML applications, the images must be persisted first. For example, Sentinel-2 imagery is available from Microsoft's Planetary Computer and filterable via STAC API: https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a. Additionally, the temporal specificity of the data allows integration with other imagery sources from the indicated timestamp, such as Landsat or other high-resolution imagery.

Source code used to generate this dataset and to use it for ML model training is available at https://github.com/SimonJasansky/mine-segmentation. It includes useful Python scripts, e.g. to download Sentinel-2 images via STAC API, or to divide tile images (2048x2048px) into smaller chips (e.g. 512x512px). 

A database schema, a schematic depiction of the dataset generation process, and a map of the global distribution of tiles are provided in the accompanying images.