--- 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 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/).