Beyond Point Predictions: Uncertainty-Aware Satellite Poverty Mapping for Public Policy

• URL: http://arxiv.org/abs/2608.23322v2
• Date: Wed, 26 Aug 2026 14:23:51 GMT
• Title: Beyond Point Predictions: Uncertainty-Aware Satellite Poverty Mapping for Public Policy
• Abstract summary: Decision-makers and analysts need assurances that they will not be misled by errors in machine learning predictions.<n>We develop an uncertainty-aware EO-ML method for poverty mapping based on simultaneous quantile regression and a novel form of conformal prediction.<n>In simulations, this approach delivers substantially more aid per eligible recipient than other strategies.
• Score: 2.681553164626998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite their critical importance for policy and research, high-resolution poverty data remain limited across much of Africa. Machine learning (ML) with earth observation (EO) imagery has recently emerged as a way to supplement these data by predicting (i.e., estimating) poverty where it has not been directly measured. Yet to be used reliably, decision-makers and analysts need assurances that they will not be misled by the errors in these predictions. To meet this need, we develop an uncertainty-aware EO-ML method for poverty mapping based on simultaneous quantile regression and a novel form of conformal prediction. Using a spatiotemporal transformer trained on sequences of Landsat and nighttime-light images, we produce prediction intervals for neighborhood-level International Wealth Index estimates across Africa which are statistically guaranteed to achieve their desired coverage rates. While our method's point-prediction performance matches the state of the art, its prediction intervals are wider than might be expected given its high $R^2$ of $0.75$. However, other models of similar accuracy likely suffer from comparable uncertainty, pointing to an inherent limitation: even with its remarkably high explanatory power, EO-ML cannot naively be relied upon for policy-making, such as when designing poverty-targeting programs. To handle this challenge, we develop a procedure to efficiently allocate aid using both ground-truth surveys and model predictions while provably ensuring the risk of excluding eligible neighborhoods remains below a prespecified level. In simulations, this approach delivers substantially more aid per eligible recipient than other strategies, thereby demonstrating that EO-ML can indeed be a reliable supplement to traditional data sources.

Related papers

• Conformal Reliability: A New Evaluation Metric for Conditional Generation [62.761166941396844]
  Conditional generative models have recently remarkable success in various applications.<n>We propose a novel evaluation metric called reliability based on prediction set at a pre-specified confidence level.<n>We introduce Conformal ReLiability (CReL), a framework that can (i) construct a prediction set and (ii) accurately optimize the reliability score within the constructed prediction set.
  arXiv  Detail & Related papers  (2026-05-29T03:52:44Z)
• Towards Generation-Efficient Uncertainty Estimation in Large Language Models [6.111244917760012]
  Uncertainty estimation is important for deploying LLMs in high-stakes applications such as healthcare and finance.<n>Existing methods require one or more full autoregressive generations to estimate uncertainty.<n>We investigate whether effective uncertainty estimation can be achieved with partial generation or even input-only information.
  arXiv  Detail & Related papers  (2026-05-07T11:39:13Z)
• PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data [36.6443700664411]
  Off-policy evaluation (OPE) methods aim to estimate the value of a new reinforcement learning (RL) policy prior to deployment.<n>We propose two approaches to construct valid confidence intervals for OPE when using data augmentation.
  arXiv  Detail & Related papers  (2025-07-26T21:51:15Z)
• PredictaBoard: Benchmarking LLM Score Predictability [50.47497036981544]
  Large Language Models (LLMs) often fail unpredictably.<n>This poses a significant challenge to ensuring their safe deployment.<n>We present PredictaBoard, a novel collaborative benchmarking framework.
  arXiv  Detail & Related papers  (2025-02-20T10:52:38Z)
• Robust Conformal Prediction Using Privileged Information [17.886554223172517]
  We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data.<n>Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption.
  arXiv  Detail & Related papers  (2024-06-08T08:56:47Z)
• Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction [0.22265536092123003]
  Unreliable predictions can occur when using artificial intelligence (AI) systems with negative consequences for downstream applications. Conformal prediction provides a model-agnostic framework for uncertainty quantification that can be applied to any dataset. In response to the increased need to report uncertainty alongside point predictions, we bring attention to the promise of conformal prediction in Earth Observation applications.
  arXiv  Detail & Related papers  (2024-01-12T07:31:21Z)
• Conservative Predictions on Noisy Financial Data [6.300716661852326]
  Price movements in financial markets are well known to be very noisy. Traditional rule-learning techniques would seek only high precision rules and refrain from making predictions where their antecedents did not apply. We apply a similar approach, where a model abstains from making a prediction on data points that it is uncertain on.
  arXiv  Detail & Related papers  (2023-10-18T09:14:19Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression [68.66245730450915]
  We develop an improved method for debiasing predictions and estimating frequentist uncertainty for practical datasets. Our main contribution is SLOE, an estimator of the signal strength with convergence guarantees that reduces the computation time of estimation and inference by orders of magnitude.
  arXiv  Detail & Related papers  (2021-03-23T17:48:56Z)
• Forecasting COVID-19 daily cases using phone call data [0.0]
  We propose a simple Multiple Linear Regression model optimised to use call data to forecast the number of daily confirmed cases. Our proposed approach outperforms ARIMA, ETS and a regression model without call data, evaluated by three point forecast error metrics, one prediction interval and two probabilistic forecast accuracy measures.
  arXiv  Detail & Related papers  (2020-10-05T18:07:07Z)
• Learning to Predict Error for MRI Reconstruction [67.76632988696943]
  We demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error. We propose a novel method that estimates the target labels and magnitude of the prediction error in two steps.
  arXiv  Detail & Related papers  (2020-02-13T15:55:32Z)
• Generating Interpretable Poverty Maps using Object Detection in Satellite Images [80.35540308137043]
  We demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to satellite images. Using the weighted counts of objects as features, we achieve 0.539 Pearson's r2 in predicting village-level poverty in Uganda, a 31% improvement over existing (and less interpretable) benchmarks.
  arXiv  Detail & Related papers  (2020-02-05T02:50:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.