DNP-XGboostCartago

Estimated building footprint layers generated by applying an XGBoost classification model to satellite imagery for the Municipality of Cartago, Colombia. The model was developed during the DataSandbox Colombia 2020 by the Data Science Unit of the National Planning Department (Departamento Nacional de Planeación). The dataset includes polygon geometries and associated classification probabilities.

Use Cases

• Classifying land use and identifying built-up areas using the polygon and probability columns.
• Validating and improving remote sensing models by analyzing the probability scores associated with each classified polygon.
• Mapping urban expansion and construction density for the municipio of Cartago based on the geospatial polygon data.

Strengths

• Data is derived from a specific machine learning methodology (XGBoost) applied to satellite imagery.
• Includes a probability score for each classification, allowing for confidence assessment.
• Uses a defined map projection (MagnaSirgas 3115) for spatial consistency.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.
• The last update timestamp is 2026-05-18, which may indicate a future date and requires verification.

Provenance

Source

Data Science Unit of the National Planning Department (Departamento Nacional de Planeación), Colombia.

Collection Method

XGBoost classification model applied to satellite imagery.

Freshness

Last updated 2026-05-18 17:31:52; freshness should be verified.

Geography

Municipality of Cartago, Colombia.