Satellite imagery has emerged as an important tool to analyse demographic, health, and development indicators. While various deep learning models have been built for these tasks, each is specific to a particular problem, with few standard benchmarks available. We propose a new dataset pairing satellite imagery and high-quality survey data on child poverty to benchmark satellite feature representations. Our dataset consists of 33,608 images, each 10 km $\times$ 10 km, from 19 countries in Eastern and Southern Africa in the time period 1997-2022. As defined by UNICEF, multidimensional child poverty covers six dimensions and it can be calculated from the face-to-face Demographic and Health Surveys (DHS) Program . As part of the benchmark, we test spatial as well as temporal generalization, by testing on unseen locations, and on data after the training years. Using our dataset we benchmark multiple models, from low-level satellite imagery models such as MOSAIKS , to deep learning foundation models, which include both generic vision models such as Self-Distillation with no Labels (DINOv2) models and specific satellite imagery models such as SatMAE. We provide open source code for building the satellite dataset, obtaining ground truth data from DHS and running various models assessed in our work.

翻译：卫星影像已成为分析人口、健康与发展指标的重要工具。尽管已针对这些任务构建了多种深度学习模型，但每种模型均针对特定问题设计，且缺乏标准基准。本文提出一个将卫星影像与高质量儿童贫困调查数据配对的新数据集，用以评估卫星特征表示的性能。该数据集包含1997-2022年间东非和南非19个国家的33,608张影像，每张影像覆盖10 km × 10 km区域。根据联合国儿童基金会的定义，多维儿童贫困涵盖六个维度，其数据可通过面对面的"人口与健康调查"项目计算获得。本基准测试通过未观测地理位置及训练年份之后的数据，对模型的空间泛化与时间泛化能力进行评估。基于该数据集，我们对从低层级卫星影像模型到深度学习基础模型进行了系统评测，包括通用视觉模型和无标签自蒸馏模型，以及专用卫星影像模型。我们开源了构建卫星数据集、从DHS获取地面真实数据及运行各类评估模型的完整代码。