This study presents a standardized, machine learning-based approach to estimate slum populations at the neighborhood level across 129 Global South countries, using satellite imagery, household surveys, and gridded population data. The methodology aligns with the UN SDG 11.1 framework and demonstrates strong performance (R² = 0.82–0.96; RMSE = 4.85–10.47%), outperforming previous benchmarks. By addressing the limitations of government-reported data, this work provides the first comprehensive geospatial inventory of slum populations, offering critical insights for urban planning and humanitarian efforts.

This is a pioneering study that leverages satellite imagery to estimate slum populations at a regional scale, with significant potential for future applications in urban policy, research, and humanitarian aid. The manuscript is well-written, and the methodology is rigorous and clearly presented. I have only a few minor suggestions for the authors to consider before publication. 

#1 The study employs a fine-tuned CNN model (ResNet) and XGBoost to classify slum households, which is innovative. However, the necessity of fine-tuning the CNN and performing extensive feature extraction is not entirely clear. It appears that classification might be achievable using the original satellite image bands without expanding the RGB inputs into 500+ features. Could the authors elaborate on the specific benefits of fine-tuning and high-dimensional feature extraction in this context? Additionally, did you evaluate the performance improvement compared to models using only raw image bands? Such comparison would help clarify the added value of the proposed approach.

#2 Another concern relates to the quality of the ground-truth labels used for training and evaluating the model. In particular, for the demographic and health surveys utilized, did the authors perform any quality assessment or validation of the labeling process? Additional details on how label reliability was ensured would strengthen the credibility of the results.

Specific comments:

Figure 2. The numbers seem confusing. Shouldn't it be from f(x)1 to f(x)7 and tree1 to tree7, layer 1 to layer 7?