README.md

---
dataset_info:
  features:
  - name: LATITUD_Y
    dtype: float64
  - name: LONGITUD_X
    dtype: float64
  - name: Municipio
    dtype: large_string
  - name: mines_outcome
    dtype: int64
  - name: elevation
    dtype: float64
  - name: rainfall
    dtype: float64
  - name: temperature
    dtype: float64
  - name: population_2012
    dtype: float64
  - name: No. Víctimas por Declaración
    dtype: int64
  - name: airports_dist
    dtype: float64
  - name: seaport_dist
    dtype: float64
  - name: settlement_dist
    dtype: float64
  - name: finance_dist
    dtype: float64
  - name: edu_dist
    dtype: float64
  - name: buildings_dist
    dtype: float64
  - name: waterways_dist
    dtype: float64
  - name: coca_dist
    dtype: float64
  - name: dist_roads_t1
    dtype: float64
  - name: dist_roads_t2
    dtype: float64
  - name: dist_roads_t3
    dtype: float64
  - name: dist_old_mine
    dtype: float64
  - name: soil_texture15_trans1
    dtype: float64
  - name: soil_texture15_trans2
    dtype: float64
  - name: nighttime_lights_2012
    dtype: float64
  - name: hist_mines
    dtype: float64
  - name: retro_pobl_tot
    dtype: float64
  - name: indrural
    dtype: float64
  - name: areaoficialkm2
    dtype: int64
  - name: altura
    dtype: float64
  - name: discapital
    dtype: float64
  - name: pib_percapita_cons
    dtype: float64
  - name: land_use_Agroforestal
    dtype: int64
  - name: land_use_Agrícola
    dtype: int64
  - name: land_use_Conservación de Suelos
    dtype: int64
  - name: land_use_Cuerpo de agua
    dtype: int64
  - name: land_use_Forestal
    dtype: int64
  - name: land_use_Ganadera
    dtype: int64
  - name: land_use_Zonas urbanas
    dtype: int64
  - name: weather_Cuerpo de agua
    dtype: int64
  - name: weather_Cálido húmedo
    dtype: int64
  - name: weather_Cálido húmedo a muy húmedo
    dtype: int64
  - name: weather_Cálido muy húmedo
    dtype: int64
  - name: weather_Cálido seco
    dtype: int64
  - name: weather_Cálido seco a húmedo
    dtype: int64
  - name: weather_Frío húmedo
    dtype: int64
  - name: weather_Frío húmedo a muy húmedo
    dtype: int64
  - name: weather_Frío húmedo y frío muy húmedo
    dtype: int64
  - name: weather_Frío muy húmedo
    dtype: int64
  - name: weather_Muy frío y muy húmedo
    dtype: int64
  - name: weather_Templado húmedo a muy húmedo
    dtype: int64
  - name: weather_Zona urbana
    dtype: int64
  - name: relief_Abanicos aluviales subactuales y recientes
    dtype: int64
  - name: relief_Abanicos aluvio-torrenciales, abanicoterrazas y glacís subrecientes
      y antiguos
    dtype: int64
  - name: relief_Cuerpo de agua
    dtype: int64
  - name: relief_Espinazos
    dtype: int64
  - name: relief_Espinazos y colinas
    dtype: int64
  - name: relief_Filas y vigas
    dtype: int64
  - name: relief_Glacís coluvial y coluvios de remoción
    dtype: int64
  - name: relief_Glacís y coluvios de remoción
    dtype: int64
  - name: relief_Lomas y colinas
    dtype: int64
  - name: relief_Plano de inundación
    dtype: int64
  - name: relief_Plano de inundación y terrazas bajas
    dtype: int64
  - name: relief_Terrazas
    dtype: int64
  - name: relief_Terrazas y abanicos terrazas
    dtype: int64
  - name: relief_Vallecitos
    dtype: int64
  - name: relief_Vallecitos coluvio-aluviales
    dtype: int64
  - name: relief_Zona urbana
    dtype: int64
  splits:
  - name: train
    num_examples: 26761
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- tabular-classification
language:
- en
- es
tags:
- landmine-detection
- humanitarian-demining
- colombia
- geospatial
- conflict
- tabular
- binary-classification
- imbalanced
size_categories:
- 10K<n<100K
pretty_name: Landmine Detection in Antioquia, Colombia
---

# Landmine Detection in Antioquia, Colombia

## Dataset Description

This dataset supports landmine risk estimation across 15 municipalities in the Antioquia department of Colombia. The region is divided into **26,761 grid cells** of 500 × 500 meters each, with **63 predictive features** derived from multiple open geospatial, socio-economic, and conflict-related data sources. Each grid cell is labeled with a binary outcome indicating whether landmines were found during humanitarian demining operations.

The dataset is heavily imbalanced: **only 1.6% of grid cells are positive** (landmine present), reflecting the real-world rarity of contaminated areas even in conflict-affected regions.

Data from:

> Dulce Rubio et al. "[RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization](https://arxiv.org/abs/2311.03115)." *ACM Journal on Computing and Sustainable Societies* 2.2 (2024): 1–29.

## Usage

### Loading with 🤗 Datasets

```python
from datasets import load_dataset

ds = load_dataset("cmpatino/landmine-detection")
df = ds["train"].to_pandas()

# Features and target
X = df.drop(columns=["LATITUD_Y", "LONGITUD_X", "Municipio", "mines_outcome"])
y = df["mines_outcome"]

print(f"Samples: {len(df)}, Features: {X.shape[1]}, Positive rate: {y.mean():.3%}")
# Samples: 26761, Features: 63, Positive rate: 1.607%
```

### Loading directly with pandas

```python
import pandas as pd

df = pd.read_parquet(
    "hf://datasets/cmpatino/landmine-detection/data/train-00000-of-00001.parquet"
)

# Quick overview
print(df.shape)            # (26761, 67)
print(df.dtypes.value_counts())
# float64    30
# int64      37

# Target distribution
print(df["mines_outcome"].value_counts())
# 0    26331
# 1      430

# Contamination rate per municipality
print(
    df.groupby("Municipio")["mines_outcome"]
    .mean()
    .sort_values(ascending=False)
    .head()
)
```

## Dataset Summary

| Property | Value |
|---|---|
| **Grid cells** | 26,761 |
| **Features** | 63 continuous + categorical (one-hot encoded) |
| **Target** | `mines_outcome` (binary: 0 = no mines, 1 = mines found) |
| **Positive rate** | 1.61% (430 positive cells) |
| **Municipalities** | 15 (all declared mine-free after comprehensive demining) |
| **Grid resolution** | 500 × 500 meters |
| **Region** | Antioquia, Colombia |

## Background

During the decades-long armed conflict in Colombia, anti-personnel landmines were extensively used by illegal armed groups to protect areas of strategic interest: coca crops, drug trafficking routes, laboratories, and illegal mining sites. Between 1990 and 2022, approximately 38,000 landmine events were recorded, resulting in over 12,000 victims (~20% fatal). Antioquia is the department with the highest historical number of landmine events and victims in Colombia.

The 15 municipalities in this dataset have been **declared mine-free** after comprehensive humanitarian demining operations, meaning the ground truth labels are based on actual demining results rather than estimates:

> Abejorral, Alejandría, Chigorodó, Cocorná, El Carmen de Viboral, Granada, La Unión, Nariño, Sabanalarga, San Carlos, San Francisco, San Luis, San Rafael, San Roque, Sonsón.

## Label Assignment

A grid cell receives a **positive label** (`mines_outcome = 1`) if it intersects at least one confirmed hazardous area or a cleared area where landmines were found during humanitarian demining operations, using data from Colombia's National Mine Action Center (AICMA). Otherwise, the cell receives a **negative label** (`mines_outcome = 0`), as these areas have been thoroughly inspected and declared free of mines.

## Features

The 67 columns are organized as follows:

### Identifiers & Target (4 columns)

| Column | Description |
|---|---|
| `LATITUD_Y` | Latitude of grid cell center |
| `LONGITUD_X` | Longitude of grid cell center |
| `Municipio` | Municipality name (15 unique values) |
| `mines_outcome` | **Target variable** — 1 if landmines found, 0 otherwise |

### Continuous Features (30 columns)

These fall into three categories based on the RELand framework:

#### Remnants of War Indicators

Features capturing the historical presence of armed groups and conflict-related infrastructure.

| Column | Description | Unit | Source |
|---|---|---|---|
| `dist_old_mine` | Distance to nearest historical landmine accident | km | DAICMA (2022) |
| `coca_dist` | Distance to nearest coca cultivation area | km | UNODC (2021) |
| `hist_mines` | Number of historical landmine accidents in municipality | incidents | DAICMA (2022) |
| `No. Víctimas por Declaración` | Declared conflict victims in municipality | people | Victims Unit (2019) |

#### Geographic & Environmental Features

Features describing the terrain, climate, and ecological characteristics of each grid cell.

| Column | Description | Unit | Source |
|---|---|---|---|
| `elevation` | Elevation | m above sea level | WorldClim (2023) |
| `rainfall` | Average annual rainfall | mm | WorldClim (2023) |
| `temperature` | Average temperature | °C | WorldClim (2023) |
| `waterways_dist` | Distance to nearest waterway | km | OpenStreetMap (2022) |
| `soil_texture15_trans1` | Soil texture component 1 (ILR-transformed) | — | Varón-Ramírez et al. (2022) |
| `soil_texture15_trans2` | Soil texture component 2 (ILR-transformed) | — | Varón-Ramírez et al. (2022) |
| `altura` | Average altitude of the municipality | m | Acevedo & Bornacelly (2014) |

#### Socio-Demographic & Infrastructure Features

| Column | Description | Unit | Source |
|---|---|---|---|
| `population_2012` | Population estimate for the grid cell (2012) | people | WorldPop (2023) |
| `airports_dist` | Distance to nearest airport | km | OpenStreetMap (2022) |
| `seaport_dist` | Distance to nearest seaport | km | OpenStreetMap (2022) |
| `settlement_dist` | Distance to nearest settlement | km | OpenStreetMap (2022) |
| `finance_dist` | Distance to nearest financial institution | km | OpenStreetMap (2022) |
| `edu_dist` | Distance to nearest school/educational institution | km | OpenStreetMap (2022) |
| `buildings_dist` | Distance to nearest building | km | OpenStreetMap (2022) |
| `dist_roads_t1` | Distance to nearest primary road | km | IGAC/Codazzi (2023) |
| `dist_roads_t2` | Distance to nearest secondary road | km | IGAC/Codazzi (2023) |
| `dist_roads_t3` | Distance to nearest tertiary road | km | IGAC/Codazzi (2023) |
| `nighttime_lights_2012` | Nighttime light intensity (2012) | index 0–63 | Li et al. (2020) |
| `retro_pobl_tot` | Total municipal population | people | Acevedo & Bornacelly (2014) |
| `indrural` | Rural index of municipality | 0 = urban, 1 = fully rural | Acevedo & Bornacelly (2014) |
| `areaoficialkm2` | Official municipal area | km² | Acevedo & Bornacelly (2014) |
| `discapital` | Distance to departmental capital (Medellín) | km | Acevedo & Bornacelly (2014) |
| `pib_percapita_cons` | GDP per capita (2009 constant prices) | millions COP | Acevedo & Bornacelly (2014) |
| `rwi` | Relative Wealth Index | index | Meta (2022) |

> **Note:** `rwi` (Relative Wealth Index) is listed in the paper's Appendix B feature table but is not present as a standalone column in this particular dataset version.

### One-Hot Encoded Categorical Features (33 columns)

These are binary (0/1) indicator columns derived from three categorical variables:

#### Land Use (7 categories, source: IGAC 2022)

`land_use_Agroforestal`, `land_use_Agrícola`, `land_use_Conservación de Suelos`, `land_use_Cuerpo de agua`, `land_use_Forestal`, `land_use_Ganadera`, `land_use_Zonas urbanas`

#### Weather/Climate Zone (13 categories, source: IGAC 2022)

`weather_Cuerpo de agua`, `weather_Cálido húmedo`, `weather_Cálido húmedo a muy húmedo`, `weather_Cálido muy húmedo`, `weather_Cálido seco`, `weather_Cálido seco a húmedo`, `weather_Frío húmedo`, `weather_Frío húmedo a muy húmedo`, `weather_Frío húmedo y frío muy húmedo`, `weather_Frío muy húmedo`, `weather_Muy frío y muy húmedo`, `weather_Templado húmedo a muy húmedo`, `weather_Zona urbana`

#### Topographic Relief (13 categories, source: IGAC 2022)

`relief_Abanicos aluviales subactuales y recientes`, `relief_Abanicos aluvio-torrenciales, abanicoterrazas y glacís subrecientes y antiguos`, `relief_Cuerpo de agua`, `relief_Espinazos`, `relief_Espinazos y colinas`, `relief_Filas y vigas`, `relief_Glacís coluvial y coluvios de remoción`, `relief_Glacís y coluvios de remoción`, `relief_Lomas y colinas`, `relief_Plano de inundación`, `relief_Plano de inundación y terrazas bajas`, `relief_Terrazas`, `relief_Terrazas y abanicos terrazas`, `relief_Vallecitos`, `relief_Vallecitos coluvio-aluviales`, `relief_Zona urbana`

## Municipality Distribution

| Municipality | Grid Cells | % of Total |
|---|---|---|
| Sonsón | 5,461 | 20.4% |
| Chigorodó | 2,937 | 11.0% |
| San Carlos | 2,911 | 10.9% |
| Abejorral | 2,051 | 7.7% |
| San Luis | 1,728 | 6.5% |
| San Roque | 1,718 | 6.4% |
| El Carmen de Viboral | 1,716 | 6.4% |
| San Rafael | 1,478 | 5.5% |
| San Francisco | 1,450 | 5.4% |
| Nariño | 1,287 | 4.8% |
| Sabanalarga | 1,074 | 4.0% |
| Cocorná | 980 | 3.7% |
| Granada | 773 | 2.9% |
| La Unión | 678 | 2.5% |
| Alejandría | 519 | 1.9% |

## Validation Protocols

The RELand paper proposes three validation protocols that account for the spatial structure of the data and simulate real demining operations:

1. **blockCV** — Block cross-validation within the Antioquia department, respecting municipal boundaries as spatial blocks.
2. **blockV** — Train on a subset of municipalities and validate on held-out municipalities (simulates deploying to a new area within the same department).
3. **blockV-OOD** — Train on Antioquia and evaluate on a completely different department (out-of-distribution generalization).

The `Municipio` column enables implementing these spatial validation schemes.

## Ethical Considerations

This dataset is intended for **research purposes** in humanitarian demining and conflict-affected area analysis. The locations in this dataset correspond to municipalities that have already been declared mine-free. Predictions from models trained on this data should **not** be used as the sole basis for demining operations without expert human review and validation in the field.

## Citation

```bibtex
@article{rubio2024reland,
  title={RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization},
  author={Rubio, Dulce and Patino, Camilo and Ramírez, Juan Felipe and Weidmann, Nils B and Müller, Emmanuel},
  journal={ACM Journal on Computing and Sustainable Societies},
  volume={2},
  number={2},
  pages={1--29},
  year={2024},
  publisher={ACM},
  doi={10.1145/3657055}
}
```

## License

This dataset is released under the [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/).