maduschek/LAMES

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

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
<!-- Motivation for the creation of this dataset. -->
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
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
**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
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
**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?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
Included Annotation of Maus et al: https://doi.pangaea.de/10.1594/PANGAEA.942325

#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

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## More Information [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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