用全球统一地表嵌入评估12城贫民窟识别与密度图，发现时间跨年训练最优。
Slum Detection and Density Mapping with AlphaEarth Foundations: A Representation Learning Evaluation Across 12 Global Cities
- 基于AlphaEarth基础嵌入，用伪标签训练贫民窟检测与密度估计
- 跨年训练效果优于跨城迁移，10米分辨率难捕捉像素内密度梯度
- 兴趣点特征提升最大，六城可生成连续多年贫民窟分布图
像素级贫民窟制图长期受限于跨城市泛化能力差、缺乏连续密度估计及全球可比性弱。AlphaEarth Foundations（AEF）是64维、10米分辨率的全球一致年度地表嵌入，为轻量级贫民窟监测提供新基础，但其在贫民窟检测——这一受建筑形态与社会经济过程共同影响的间接任务——中的适用性尚未验证。本研究在12个城市的69个城-年对（2017–2024）上评估AEF在贫民窟分类与亚像素密度估计的表现，采用GRAM伪掩码作为监督标签。涵盖四种训练策略、两种协议（随机划分与3×3空间块交叉验证）、六种辅助特征配置及五种基线模型，并辅以表示层分析（PCA、SHAP）与全区域覆盖推断。五大发现：（1）同城市跨年训练在两种协议下均最优（中位空间F1=0.616，R²=0.466）；时间扩展优于跨城迁移，表明城市尺度表征漂移显著；（2）回归R²主要由零/非零边界判别驱动：所有城市正像素R²均为负值，揭示10米分辨率下难以建模像素内密度梯度；（3）PC36在各任务中始终领先；分类在k=32饱和，回归在k=64仍未饱和；（4）兴趣点（POI）特征带来最大密度提升（ΔR²=+0.064）；（5）六座满足双任务可用性阈值的城市，全区域覆盖推断（2017–2024）保持贫民窟簇结构稳定（平均SSIM=0.926）。研究厘清了基础模型嵌入在贫民窟监测中的能力边界与互补需求。
原文摘要 · Abstract (English)
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.
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