Context-Aware Slum Mapping in Sub-Saharan Africa Using Sentinel-1 Texture and Local Climate Zones

2026-07-08

AI Summary

This study addresses the challenge of distinguishing spectrally similar informal settlements (Local Climate Zone 7, LCZ 7) from formal low-density built-up areas (LCZ 3) in sub-Saharan African cities using optical imagery alone, which limits mapping accuracy. To overcome this, the authors propose a context-aware optical–SAR fusion framework that integrates Sentinel-2 spectral data with Sentinel-1 structural features. The approach innovatively incorporates physics-informed SAR feature engineering and a three-level fusion mechanism, and— for the first time—employs GLCM texture and structural disorder metrics for LCZ 7 identification. Experimental results demonstrate overall accuracies of 0.816 and 0.807 in dry and wet seasons, respectively, substantially outperforming the WUDAPT baseline (0.704) and reducing LCZ 3/7 confusion to 7%. These findings confirm the critical role of SAR-derived textures in enabling robust, multi-seasonal, and cross-city identification of informal settlements.

Abstract

Accurate mapping of informal settlements remains a major challenge in Sub-Saharan African (SSA) cities because optical imagery often fails to distinguish Informal Settlements (defined here as LCZ 7) from spectrally similar formal Compact Low-Rise areas (LCZ 3). This study presents a context-aware, reproducible Optical-SAR framework that improves informal settlement delineation using Sentinel-2 spectral features and Sentinel-1 structural information within an adapted Local Climate Zone (LCZ) taxonomy. We implement a three-tier SAR integration strategy: calibrated backscatter, GLCM textures, and a physics-guided feature engineered to capture the high structural disorder and weak radar return characteristic of SSA informal settlements. Using reference data across Nairobi and Eldoret (Kenya), we evaluate performance via a stratified hold-out protocol and a season-aware ablation study. Results show that SAR textures provide the dominant performance gain for LCZ 7 detection. The Optical-SAR model achieves overall accuracy of 0.816 (dry) and 0.807 (wet), significantly outperforming the WUDAPT baseline (OA 0.704) and reducing the critical LCZ 3 - LCZ 7 confusion to 7%. Seasonal analysis indicates that while optical-only separability varies with phenology, SAR-derived textures stabilize informal settlement mapping across seasons. These findings demonstrate that the incorporation of SAR-derived features yields consistent improvements for urban morphology mapping in data-scarce environments across seasons and across the evaluated source cities, while cross-city transfer remains limited without local adaptation strategies.

Problem

• slum mapping
• informal settlements
• Local Climate Zones
• optical imagery
• urban morphology

Innovation

• Optical-SAR fusion
• Sentinel-1 texture
• Local Climate Zones
• informal settlement mapping
• context-aware framework