High-Resolution Rural Poverty Mapping for Sindh – Research-to-Policy Pilot

Overview

The High-Resolution Rural Poverty Mapping initiative in Sindh Province, Pakistan, is a research-to-policy pilot that uses ensemble deep learning applied to satellite imagery to generate 1 km² resolution poverty predictions for rural areas. The project was developed by an academic research team comprising Felix Agyemang (University of Manchester), Rashid Memon (University of Qatar), Levi John Wolf and Sean Fox (University of Bristol), in collaboration with the Strategic Social Protection Unit (SPSU) of Pakistan's Sindh Province. Funded by the Center for Effective Global Action's (CEGA) Targeting Aid Better Initiative at UC Berkeley, the initiative was motivated by the twin shocks of the Covid-19 pandemic (March 2020) and one of the wettest monsoon seasons since 1961, which devastated rural households in Sindh and created an urgent need to identify communities eligible for emergency cash relief without relying on costly and time-consuming household surveys.

The system employs a transfer learning approach using three convolutional neural network (CNN) architectures — ResNet-50, ResNet-50V2, and ResNet-101 — initialised with ImageNet weights and trained end-to-end on three concatenated input streams. The primary data inputs are Sentinel-2 daytime satellite imagery (10 m resolution, captured January–April 2016), VIIRS nighttime lights data (2016 median annual product, resampled from 500 m to 10 m), and a global accessibility layer measuring travel time to settlements of 5,000–10,000 population. The three CNN models are combined into an ensemble using a modal (majority vote) classification approach, producing binary predictions at the 1 km² grid cell level: cells with median Simple Poverty Scorecard (SPS) scores below 19 are classified as chronically poor, while those at or above 19 are classified as not chronically poor.

The training data comprised approximately 1.67 million anonymised household-level poverty score records (from an original collection of approximately 1.9 million), gathered through computer-assisted personal interviews (CAPI) on Android tablets between 2016 and 2019. These records were collected under two existing Sindh government programmes: the Sindh Union Council Economic Strengthening Support (SUCCESS) programme, covering 8 districts, and the People's Poverty Reduction Program (PPRP), covering 6 additional districts — together spanning 14 of Sindh's 24 districts. The SPS methodology, originally adopted by the Government of Pakistan as the targeting tool for the Benazir Income Support Program (BISP), its flagship cash transfer programme since January 2009, assigns households a score from 0 to 100, where higher scores indicate less deprivation.

The research team implemented a rigorous three-stage validation framework. First, a hold-out test on randomly sampled data showed the ensemble model achieving 71% recall, 67% precision, 69% overall accuracy, and an AUC of 0.69, outperforming a comparable Uganda study (66% recall, 44% precision). Second, six-fold spatial cross-validation — where entire districts were held out in turn — demonstrated ensemble recall of 59–81% and median AUC of 0.66, with the ensemble consistently performing as the most stable predictor across iterations. Third, a novel ground-truth validation exercise was conducted in Ghotki district in 2022, surveying approximately 7,000 households across 174 grid cells, where the ensemble model achieved 59% recall versus 50% for random classification. The models were trained using Keras and TensorFlow in Python with a batch size of 16, learning rate of 0.00005, 30 epochs, and early stopping after 10 consecutive epochs without validation improvement.

The resulting poverty maps cover 32,281 grid cells across rural Sindh at 1 km² resolution, representing a tenfold improvement in spatial granularity over prior geographic targeting models. The SPSU intended to use these maps to develop a targeting strategy for social welfare interventions, particularly emergency cash relief. However, the SPSU subsequently launched a USD 230 million initiative with the World Bank — the Strengthening Social Protection Delivery System in Sindh (SSPDSS) project — that relies on a Multidimensional Poverty Index (MPI) for geographic targeting instead of the ML-generated poverty prevalence estimates (CEGA, 2023b; World Bank, 2022). No primary evidence has been identified confirming that the poverty maps were operationally adopted into government targeting workflows or that they directly informed benefit allocation decisions. The project remains characterised as a research-to-policy initiative with demonstrated technical feasibility but no documented policy uptake.

The study received ethical approval from the Institutional Review Board at Lahore University of Management Sciences (LUMS-IRB/05/30/2022/RM-FWA-00019408). Verbal informed consent was obtained from all households using a standardised consent script, and all data was aggregated to 1 km² cells to prevent individual household identification. All data files and scripts were made publicly available on Figshare. Key limitations acknowledged include substantial spatial noise in the original GPS-referenced survey data, the asset-based SPS measure differing from the official consumption-based poverty line, geographic coverage limited to 14 of 24 districts, and temporal separation between 2016 training imagery and 2022 validation data.

Classification

Programme Details

Research-to-policy pilot using ensemble deep learning on satellite imagery to produce 1 km² resolution rural poverty maps for Sindh Province. Developed to support the Strategic Social Protection Unit (SPSU) in geographic targeting for emergency cash relief during Covid-19 and monsoon flood response, building on existing household poverty score data from the SUCCESS and PPRP programmes.

Implementation Details

Risk & Oversight

Deployment & Outcomes

Sources

1. SRC-002-PAK-001 Center for Effective Global Action (CEGA) (2023) 'Combining satellite imagery and machine learning to target social protection in Pakistan'. Berkeley: UC Berkeley. Available at: https://cega.berkeley.edu/collection/ml-targeting-sindhpakistan/ (Accessed: 23 March 2026).
   https://cega.berkeley.edu/collection/ml-targeting-sindhpakistan/
2. SRC-003-PAK-001 Center for Effective Global Action (CEGA) (2023b) 'Using Ensemble Deep Learning to Deliver Aid Better in Post-Flood Pakistan', CEGA Blog, 15 May. Available at: https://cega.berkeley.edu/article/using-ensemble-deep-learning-to-deliver-aid-better-in-post-flood-pakistan-2/ (Accessed: 24 March 2026).
   https://cega.berkeley.edu/article/using-ensemble-deep-learning-to-deliver-aid-better-in-post-flood-pakistan-2/
3. SRC-001-PAK-001 Agyemang, F. S. K., Memon, R., Wolf, L. J. and Fox, S. (2023) 'High-resolution rural poverty mapping in Pakistan with ensemble deep learning', PLOS ONE, 18(4), e0283938. Available at: https://doi.org/10.1371/journal.pone.0283938 (Accessed: 23 March 2026).
   https://doi.org/10.1371/journal.pone.0283938
4. SRC-004-PAK-001 World Bank (2022) 'Factsheet: Strengthening Social Protection Delivery System in Sindh', 19 December. Available at: https://www.worldbank.org/en/news/factsheet/2022/12/19/factsheet-strengthening-social-protection-delivery-system-in-sindh (Accessed: 24 March 2026).
   https://www.worldbank.org/en/news/factsheet/2022/12/19/factsheet-strengthening-social-protection-delivery-system-in-sindh