Add dataset card with data details, split, class schema, and Croissant notes
Browse files
README.md
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---
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pretty_name: GoldMiningMDD
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license: other
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task_categories:
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- image-segmentation
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task_ids:
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- semantic-segmentation
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language:
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- en
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tags:
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- remote-sensing
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- geospatial
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- drone-imagery
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- semantic-segmentation
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size_categories:
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- n<1K
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---
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# GoldMiningMDD
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GoldMiningMDD is a drone orthomosaic semantic-segmentation dataset for mining-site analysis in Madre de Dios, Peru.
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- 12 sites
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- 24 PNG files total (12 images + 12 labels)
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- Splits: train/val/test at site level
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- Labels: 14 foreground classes (`1..14`) + background (`0`)
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## Directory structure
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```text
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train/
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image/*.png
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label/*.png
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val/
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image/*.png
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label/*.png
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test/
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image/*.png
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label/*.png
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```
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## Split definition
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- **train (4 sites):** `AcumulacionAaron2B`, `Kotsimba`, `Los5Rebeldes`, `PlayaMirador1`
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- **val (3 sites):** `Clavelito`, `Nayda`, `PlayaMirador2`
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- **test (5 sites):** `Anel`, `ElEngano`, `Linda`, `Paolita`, `SantaInesDosMil`
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`PlayaMirador` is split into two non-overlapping halves:
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- `PlayaMirador1`: bottom half (train)
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- `PlayaMirador2`: top half (val)
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## Label schema (merged classes)
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Class IDs are globally consistent across all sites.
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| ID | Class |
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|---:|---|
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| 0 | Background |
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| 1 | Building |
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| 2 | Mining raft |
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| 3 | Primary Forest |
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| 4 | Heavy machinery |
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| 5 | Water bodies |
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| 6 | Agricultural crop |
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| 7 | Compact mounds |
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| 8 | Gravel mounds |
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| 9 | Grass |
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| 10 | Type 1 natural regeneration |
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| 11 | Type 2 natural regeneration |
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| 12 | Bare ground |
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| 13 | Sluice |
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| 14 | Vehicles |
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### Merge rules from original 19-class labels
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- `Building` = original IDs `{1 (Urban area), 4 (Mining camp)}`
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- `Heavy machinery` = `{5 (Front loader), 8 (Excavator), 9 (Heavy machinery), 19 (Dump truck)}`
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- `Vehicles` = `{17 (Vehicles), 18 (Small vehicles)}`
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- Other classes map 1-to-1 to their corresponding semantic category.
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## Image-label pairing
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- Pairing is by basename under each split:
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- `train/image/AcumulacionAaron2B.png` ↔ `train/label/AcumulacionAaron2B.png`
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- Label rasters are aligned to image spatial extent and grid before export.
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## Spatial/reference notes
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- Source CRS in preprocessing: EPSG:32719
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- This repository stores aligned PNG exports for training/evaluation.
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- Full spatial metadata and preprocessing details are maintained in the project-side documentation.
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## Recommended usage
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- Treat label value `0` as background/ignore if your training protocol does not score background.
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- Keep site-level split boundaries (no cross-split mixing of sites).
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## Croissant status
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The Hub `/croissant` endpoint currently returns unavailable for this dataset (no Hub-parquet representation yet).
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Endpoint:
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```text
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https://huggingface.co/api/datasets/kangnicui2/GoldMiningMDD/croissant
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```
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To get auto-generated Croissant on Hugging Face, publish the dataset in a form that the Dataset Viewer can convert to Parquet (or already provides Parquet). Then the `/croissant` endpoint is generated automatically by the Hub.
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