Slums are defined at the household level by deficiencies in housing and basic services, and their identification is central to understanding and addressing urban deprivation. Previous studies relying on very-high resolution imagery and deep learning method often involve costly data acquisition, intensive computational requirements, and limited transparency in model interpretation. To address these challenges, we propose a building-level slum mapping framework that directly classifies individual buildings using a Random Forest model. The framework leverages explicitly semantic morphometrics from open building footprint data, complemented by spectral, proximity-based, and topographic features, all based on publicly available sources. The model was trained and validated on labeled data from over 250,000 buildings across four major cities in Kenya. Under K-fold cross-validation, the full-feature model achieved strong performance (F1 score = 0.987), compared to 0.836 when using morphological features alone. Spatial cross-validation further demonstrated that combining morphological and spectral features yielded the highest average F1 score (0.742), indicating stable generalization to unseen cities. These findings highlight the value of building-level morphometrics for cost-effective and transferable slum mapping. To support broader applications and reduce the risk of stigmatizing individual households, predicted slum buildings are aggregated into 100-meter grid cells, providing a scalable basis for urban vulnerability assessment and sustainable urban planning.
Yang et al. (Thu,) studied this question.