--- license: cc-by-4.0 dataset_info: features: - name: image dtype: image - name: mask dtype: image splits: - name: train num_bytes: 13070075868 num_examples: 168510 - name: validation num_bytes: 3109460987 num_examples: 41591 - name: test num_bytes: 3578191881 num_examples: 43630 download_size: 20864559061 dataset_size: 19757728736 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for Dataset Name A mining site dataset to identify mining sites on rocky / deserty / non-vegetated land surfaces. The dataset depicts large scale mining sites of the country of chile. ## Dataset Details ### Dataset Description A mining site dataset to identify mining sites on rocky / deserty / non-vegetated land surfaces. The dataset depicts large scale mining sites of the country of chile. - **Curated by:** Matthias Kahl (https://github.com/maduschek) - **Funded by [optional]:** DynamicEarthNet and the Future Lab AI4EO - **Shared by [optional]:** [More Information Needed] - **License:** [More Information Needed] - **Attribution:** Mineral Resources Engineering (MRE) of RWTH Aachen ### Dataset Sources [optional] - **Repository:** https://github.com/maduschek/mine-sector-detection - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses Organized as binary semantic segmentation dataset. ### Direct Use [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Dataset Structure **Dataset Fields** | **Class** | **segments** | **Key** | **px mask** | |----------------------------|--------------|----------|--------------| | other | - | - | `0` | | ASM site | - | `asm` | `1` | | LSM site | `150` | `lsm` | `2` | | open pit | `143` | `op` | `5` | | mine facility | `416` | `mf` | `4` | | waste rock dump | `320` | `wr` | `9` | | stockyard | `158` | `sy` | `7` | | processing plant | `80` | `pp` | `6` | | tailings storage facility | `59` | `tsf` | `8` | | heap leaching | `31` | `lh` | `3` | **Project Folder Structure** ```text project/ ├── annotations/ │ ├── annotation_doc.pdf │ ├── Chile_LSM_sectors.geojson │ ├── Chile_LSM_sites_Maus_et_al_subset.geojson │ ├── Ghana_ASM.geojson │ ├── overview.qgs~ │ ├── overview.qgz │ ├── test_sites.geojson │ └── train_sites.geojson │ ├── example Images/ │ ├── hires_msk_patch.png │ ├── hires_tci_patch.png │ ├── lh/ │ ├── s2_msk_patch.png │ ├── s2_msk_patch.tif │ ├── s2_tci_patch.png │ ├── s2_tci_patch.tif │ ├── train_test_sites_map_lowres.pdf │ └── train_test_sites_map.pdf │ ├── img_sector/ │ ├── multiclass_image_data/ │ └── multiclass_image_data.zip │ ├── img_site/ │ └── image-data.tar.gz │ ├── metadata/ │ ├── metadata.csv │ ├── metadata_sources.txt │ └── metadata.xlsx │ └── results/ ├── sector_classification.txt ├── site_detection_10k_rand.txt └── site_detection.txt 📘 **Caption:** The structure of the project folder, including annotations, image data, metadata, and results files. [More Information Needed] ## Dataset Creation ### Curation Rationale Large-Scale Mining (LSM) operations play a crucial role in the economic development of many nations. However, their activities are a major driver of land-use change, high energy consumption, and a range of environmental impacts such as soil erosion, deforestation, and water pollution. The influence of LSM often extends far beyond the boundaries of the mine itself, affecting surrounding ecosystems and communities. Monitoring these large-scale operations is therefore essential to assess their environmental footprint and to distinguish them from small-scale or artisanal mining activities. Remote sensing-based observation of LSM sites enables continuous tracking of changes in land cover, infrastructure expansion, and waste management practices. Such monitoring not only supports the detection of unregulated or illegal activities but also contributes to a better understanding of how specific mine characteristics—such as commodity type, extraction methods, and processing techniques—relate to their environmental consequences. [More Information Needed] ### Source Data **Metadata sources** http://www.andeangeology.cl/ http://www.cegmining.com/ http://www.colegiodegeologos.cl/ https://elpinguino.com/ https://infofirma.sea.gob.cl/ https://kghm.com/ https://maps.mineriaabierta.cl/ https://minedocs.com/ https://miningdataonline.com/ https://pilotaje.cl/ https://s28.q4cdn.com/ https://sec.report/ https://thediggings.com/ https://www.alertaislariesco.cl/ https://www.alxar.cl/ https://www.aminerals.cl/ https://www.angloamerican.com/ https://www.annualreports.com/ https://www.antofagasta.co.uk/ https://www.barrick.com/ https://www.britannica.com/ https://www.cap.cl/ https://www.cenizas.cl/ https://www.cmp.cl/ https://www.codelco.com/ https://www.fluor.com/ https://www.guiaminera.cl/ https://www.lundinmining.com/ https://www.mch.cl/ https://www.minainvierno.cl/ https://www.mining-technology.com/ https://www.miningnewsfeed.com/ https://www.miningweekly.com/ https://www.teck.com/ https://www.yamana.com/ https://www.youtube.com/ https://xtractresources.com/ #### Data Collection and Processing **Mining Site Detection (binary semantic segmentation)** - selection of 150 mid-sized mining sites in Chile with ~1000km² - get location of known mining sites from https://doi.pangaea.de/10.1594/PANGAEA.942325 - take the 35 spatially most isolated mining sites as TestSet, the remaining 104 mining sites as TrainingSet (note: a few mining sites were merged due to overlap) - cut the mining sites with a fair amount of surroundings from cloudless Sentinel-2 imagery - split imagery into 256x256 sized raster patches - create mask files according to geojson annotation files ![image](https://cdn-uploads.huggingface.co/production/uploads/675fed0b5679c80ce591ad1d/-2-QugZc3oUPvFL2-6Jae.png) ![image](https://cdn-uploads.huggingface.co/production/uploads/675fed0b5679c80ce591ad1d/js9Del6ClQvJ1xEjqUr7r.png) **Mine Sector Classification (multiclass semantic segmentation)** [More Information Needed] #### Who are the source data producers? Mineral Resources Engineering (MRE) of RWTH Aachen and the Chair of Data Science in Earth Observation, Technical University of Munich [More Information Needed] ### Annotations [optional] Included Annotation of Maus et al: https://doi.pangaea.de/10.1594/PANGAEA.942325 #### Annotation process [More Information Needed] #### Who are the annotators? Mineral Resources Engineering (MRE) of RWTH Aachen and the Chair of Data Science in Earth Observation, Technical University of Munich [More Information Needed] #### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]