reviews: 
Comments: The paper "KidSat: Satellite Imagery to Map Childhood Poverty" presents a dataset and benchmarking framework aimed at improving the analysis of child poverty indicators in Eastern and Southern Africa through satellite imagery. This research provides a dataset consisting of 33,608 images covering 16 countries from 1997 to 2022, paired with high-quality survey data on multidimensional child poverty factors such as housing, sanitation, water, nutrition, education, and health. By leveraging geocoded Demographic and Health Surveys data, the authors have created a resource for the social science and global health community, enabling detailed analyses across both spatial and temporal dimensions.
This work appears notable for its systematic comparison of feature representations, ranging from low-level satellite models like MOSAIKS to state-of-the-art deep learning foundation models, including DINOv2 and SatMAE. The authors assess each model's capacity for spatial generalization across different locations and temporal generalization beyond the training years, providing a framework to evaluate model efficacy across varied contexts.
Strengths: Originality
The "KidSat" paper presents a dataset that combines high-resolution satellite imagery with multidimensional child poverty data from Demographic and Health Surveys (DHS) across 16 countries in Eastern and Southern Africa. This dataset applies satellite imagery to estimate socioeconomic indicators related to child poverty, focusing on UNICEF’s six poverty dimensions—housing, water, sanitation, nutrition, health, and education. The dataset introduces a method for analyzing child-specific poverty indicators rather than general economic metrics.

Quality
The paper follows a methodical approach, providing reproducible steps for dataset construction and benchmark testing. The authors compare a range of models, including Gaussian Process Regression, MOSAIKS, DINOv2, and SatMAE, to assess performance on spatial and temporal generalization tasks. Fine-tuning, spatial and temporal benchmarks, and cross-validation are used to evaluate each model’s generalization capability.

Clarity
The paper is structured to clearly present the models, dataset, and evaluation metrics. The benchmark results, including error metrics for each model type, allow for straightforward comparisons of model performance in child poverty prediction. Visual aids, such as the heatmap of survey cluster locations and summary tables, provide concise representations of key findings.

Significance
This research provides a benchmark for child poverty estimation using satellite imagery, offering potential applications for data-informed policy and social science research in regions lacking detailed survey data. The dataset and benchmark may support further studies in poverty estimation using satellite data. Given the global availability of satellite imagery, this framework could be adapted to analyze other social or environmental indicators.
Weaknesses: 1. Limited Exploitation of Multispectral Data
While the "KidSat" dataset includes multispectral satellite imagery from sources like Sentinel-2, the study does not fully utilize the additional spectral bands beyond RGB. The paper mentions that DINOv2 relies solely on RGB bands, potentially limiting the model’s ability to capture information relevant to socioeconomic indicators, such as vegetation health or water bodies. Future work could include experiments with multispectral configurations or foundation models designed to leverage the entire spectral range available in Sentinel and Landsat imagery.

2. Constraints on Temporal Prediction Capability
The temporal benchmark reveals challenges in predicting poverty indicators in years beyond the training period (2020–2022). This suggests that the current models may be overfitting to historical data patterns and struggle to generalize to newer datasets. Incorporating temporal encoding or exploring time-series models may improve the framework’s capability to handle time-sensitive socioeconomic changes. Furthermore, training models with time-stamped data could aid in detecting trends, which would be valuable for forecasting.

3. Fixed Input Size and Resizing Effects on Model Performance
The study limits DINOv2 and SatMAE’s performance by requiring the imagery to be resized to a specific input size (224×224 for certain configurations). Resizing can lead to information loss, particularly when working with high-resolution satellite images. Evaluating models with variable or adaptive input sizes, or employing patch-based processing for high-resolution images, might preserve more spatial detail and enhance prediction accuracy.

4. Absence of Uncertainty Estimation
The current approach is deterministic and does not quantify uncertainty in model predictions, which is crucial for policymakers who may rely on this data to inform decisions. Including uncertainty metrics, such as confidence intervals or probabilistic models, would provide a more comprehensive assessment of model reliability, particularly in areas with sparse data coverage.

5. Limited Comparison with Non-ViT Architectures
Although the study highlights the use of Vision Transformers (ViTs), the experiments with alternative architectures, like CNN-based models, are limited and do not include any recent transformer alternatives. Comparing ViTs with other state-of-the-art architectures, particularly those optimized for spatial analysis, would help clarify the advantages and limitations of ViTs in the context of satellite imagery-based poverty estimation.

6. Dataset Coverage and Geographic Bias
The dataset, while extensive, is geographically focused on Eastern and Southern Africa. This focus may limit the applicability of the benchmark models to other regions with different socioeconomic and environmental conditions. An expanded dataset that includes diverse regions globally could help generalize the findings and provide a more comprehensive tool for poverty estimation.

7. Lack of Direct Validation with Ground Truth
The study’s reliance on DHS survey data as ground truth is practical, but there is limited discussion on validating model predictions with additional sources or cross-referencing with other poverty metrics. Incorporating data from other surveys, censuses, or economic indicators could enhance model robustness and provide a broader validation framework.
Recommendation: 3
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 1