Work

Addressing data imbalance in urban informal settlement mapping from earth observation using ensemble learning: A case study in Rio de Janeiro

Year: 2025

Type: article

Abstract: Informal settlements pose major challenges for public health, infrastructure, and urban planning due to their high density and unregulated growth. Remote sensing has emerged as a key tool for mapping these areas, but strong class imbalance - where informal settlements represent a small fraction of urban land - remains a critical barrier. Existing methods often rely on simple undersampling, discarding valuable training data from formal residential zones. We propose BALISE, a novel ensemble learning approach that leverages the full extent of available data to improve informal settlement detection from remote sensing. Our framework combines Sentinel-2 multispectral imagery and the Copernicus Digital Elevation Model with auxiliary features derived from OpenStreetMap. We evaluate our method on a use case in Rio de Janeiro, using a spatial cross-validation strategy that rigorously tests generalization across five different urban zones. BALISE improves both F1-score and Kappa coefficient by approximately 2 points over standard undersampling, offering a robust and transferable tool for remote sensing-based urban analysis in fragmented and socially complex environments.

Source: Science of Remote Sensing

DOI: 10.1016/j.srs.2025.100273