| --- |
| 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/). |
|
|
