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landmine-detection

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humanitarian-demining
colombia
geospatial
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metadata 
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." ACM Journal on Computing and Sustainable Societies 2.2 (2024): 1–29.

Usage

Loading with 🤗 Datasets 

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 

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 

@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.