# Generating Interpretable Poverty Maps using Object Detection in Satellite Images

arXiv: https://arxiv.org/abs/2002.01612 · HTML: https://mlsanity.com/paper/2002.01612/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 27 of 27)

- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [DiffusionSat: A Generative Foundation Model for Satellite Imagery](https://mlsanity.com/paper/2312.03606/) (2023)
- [Fast TreeSHAP: Accelerating SHAP Value Computation for Trees](https://mlsanity.com/paper/2109.09847/) (2021)
- [Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis](https://mlsanity.com/paper/2106.11485/) (2021)
- [UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Socioeconomic Indicator Prediction](https://mlsanity.com/paper/2403.16831/) (2024)
- [IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling](https://mlsanity.com/paper/2112.09126/) (2021)
- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
- [Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models](https://mlsanity.com/paper/2505.08833/) (2025)
- [A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities](https://mlsanity.com/paper/2308.00465/) (2023)
- [SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models](https://mlsanity.com/paper/2406.10225/) (2024)
- [Land Cover Mapping in Limited Labels Scenario: A Survey](https://mlsanity.com/paper/2103.02429/) (2021)
- [Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India](https://mlsanity.com/paper/2402.13796/) (2024)
- [Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution](https://mlsanity.com/paper/2204.01736/) (2022)
- [Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities](https://mlsanity.com/paper/2505.15870/) (2025)
- [A satellite foundation model for improved wealth monitoring](https://mlsanity.com/paper/2604.23166/) (2026)
- [Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain](https://mlsanity.com/paper/2203.01068/) (2022)
- [Interpreting wealth distribution via poverty map inference using multimodal data](https://mlsanity.com/paper/2302.10793/) (2023)
- [Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks](https://mlsanity.com/paper/2312.00416/) (2023)
- [Space to Policy: Scalable Brick Kiln Detection and Automatic Compliance Monitoring with Geospatial Data](https://mlsanity.com/paper/2412.04065/) (2024)
- [Seeing poverty from space, how much can it be tuned?](https://mlsanity.com/paper/2107.14700/) (2021)
- [Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural India](https://mlsanity.com/paper/2304.13958/) (2023)
- [Predicting household socioeconomic position in Mozambique using satellite and household imagery](https://mlsanity.com/paper/2411.08934/) (2024)
- [ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes](https://mlsanity.com/paper/2506.14832/) (2025)
- [Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery](https://mlsanity.com/paper/2602.17605/) (2026)
- [SatBLIP: Context Understanding and Feature Identification from Satellite Imagery with Vision-Language Learning](https://mlsanity.com/paper/2604.14373/) (2026)
- [Joint Utilization of Geospatial and census proxies for Autoencoder-Assisted Downscaling (JUGAAD) of socioeconomic indicators in India](https://mlsanity.com/paper/2607.20559/) (2026)

## Abstract (arXiv)

Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources. Recent computer vision advances in using satellite imagery to predict poverty have shown increasing accuracy, but they do not generate features that are interpretable to policymakers, inhibiting adoption by practitioners. Here we demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to high resolution (30cm) satellite images. Using the weighted counts of objects as features, we achieve 0.539 Pearson's r^2 in predicting village-level poverty in Uganda, a 31% improvement over existing (and less interpretable) benchmarks. Feature importance and ablation analysis reveal intuitive relationships between object counts and poverty predictions. Our results suggest that interpretability does not have to come at the cost of performance, at least in this important domain.