Slum Detection and Density Mapping with AlphaEarth Foundations: A Representation Learning Evaluation Across 12 Global Cities
2026-05-11 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors tested a new global data tool called AlphaEarth Foundations (AEF) to identify slums in 12 cities from 2017 to 2024. They found that training the model on the same city across different years works best, while transferring knowledge between cities is less reliable. The tool struggles to measure fine differences in slum density within small areas but can distinguish slum boundaries well. Including points of interest (POI) data helps improve density estimates. Overall, the authors show the strengths and limits of using AEF for monitoring slums over time.
AlphaEarth Foundations (AEF)slum mappingcross-city generalisationsub-pixel density estimationGRAM pseudo-masksspatial F1 scoreR-squared (R2)principal component analysis (PCA)points of interest (POI)structural similarity index (SSIM)
Authors
Shuyang Hou, Ziqi Liu, Haoyue Jiao, Zhangyan Xu, Xiaopu Zhang, Lutong Xie, Yaxian Qing, Jianyuan Liang, Xuefeng Guan, Huayi Wua
Abstract
Pixel-level slum mapping has long been constrained by limited cross-city generalisation, the absence of continuous density estimation, and weak global comparability. AlphaEarth Foundations (AEF), a globally consistent 64-dimensional annual surface embedding at 10 m, offers a new analysis-ready basis for lightweight slum monitoring, but its applicability to slum detection - an indirectly coupled task shaped by both built form and socio-economic processes - remains untested. We evaluate AEF on slum classification and sub-pixel density estimation across 12 cities and 69 city-year pairs (2017-2024), using GRAM pseudo-masks as supervisory labels. The evaluation spans four training strategies, two protocols (random split and 3x3 spatial block cross-validation), six auxiliary feature configurations, and five baseline models, complemented by representation-level analyses (PCA, SHAP) and full-AOI mapping. Five findings emerge. (1) Same-city cross-year training is optimal under both protocols (median spatial F1 = 0.616, R^2 = 0.466); temporal expansion outperforms cross-city transfer, indicating city-scale representational drift. (2) Regression R^2 is driven primarily by zero/non-zero boundary discrimination: positive-pixel R^2 is consistently negative across all cities, revealing limited capacity to model intra-pixel density gradients at 10 m. (3) PC36 is consistently top-ranked across tasks; classification saturates at k = 32 while regression remains unsaturated at k = 64. (4) POI features yield the largest density gain (Delta R^2 = +0.064). (5) For six cities meeting dual-task usability thresholds, full-AOI inference across 2017-2024 preserves slum cluster structure (mean SSIM = 0.926). The study delineates the capabilities and complementarity needs of foundation-model embeddings for slum monitoring.