Reducing global poverty, particularly in low- and middle-income countries, is a critical objective of the sustainable development goals (SDGs). To track progress toward these goals, high-frequency, granular geo-temporal data that capture changes at the neighborhood level is essential for researchers and policymakers. Recent advancements in methodology have combined machine learning (ML) and Earth observations (EOs) for poverty estimation, thereby addressing significant data gaps. However, a notable limitation of these EO-ML methods is their frequent deployment without a robust mechanism to quantify predictive uncertainty. Understanding this uncertainty is crucial for making informed decisions, effectively managing risks, and instilling confidence in users and stakeholders regarding the model’s predictions. Although deep learning (DL) offers methods to quantify predictive uncertainties, their reliability is often constrained, failing to accurately reflect the underlying variations in predictions. Our proposed method aims to enhance confidence in predictive uncertainty without sacrificing accuracy. It begins by integrating an external model to explicitly capture data variability. Subsequently, we employ two orthogonal metrics—accuracy and uncertainty—to evaluate the influence of training data, especially in scenarios involving satellite imagery (e.g., selecting a subset of source domain countries for prediction in the target country). By applying these metrics, we formulate criteria to assess the importance of choosing specific countries from the source domain as training data. Our analysis highlights the effectiveness of this methodology in situations where the target country offers high-dimensional data, like satellite images, but faces a shortage of adequate training samples for DL models.

To combat poor health and living conditions, policymakers in Africa require temporally and geographically granular data measuring economic well-being. Machine learning (ML) offers a promising alternative to expensive and time-consuming survey measurements by training models to predict economic conditions from freely available satellite imagery. However, previous efforts have failed to utilize the temporal information available in earth observation (EO) data, which may capture developments important to standards of living. In this work, we develop an EO-ML method for inferring neighborhood-level material-asset wealth using multi-temporal imagery and recurrent convolutional neural networks. Our model outperforms state-of-the-art models in several aspects of generalization, explaining 72% of the variance in wealth across held-out countries and 75% held-out time spans. Using our geographically and temporally aware models, we created spatio-temporal material-asset data maps covering the entire continent of Africa from 1990 to 2019, making our data product the largest dataset of its kind. We showcase these results by analyzing which neighborhoods are likely to escape poverty by the year 2030, which is the deadline for when the Sustainable Development Goals (SDG) are evaluated.

It is well known that SAR imagery is affected by SAR geometric distortions due to the SAR imaging process (i.e. Layover, Foreshortening and Shadows). Specially in mountainous areas these distortions affect large portions of the images and in some applications, these areas shouldn't be included in analysis. Using a foreshortening mask is a suitable solution, but finding the mask is challenging. The aim of this research is to exploit the fusion of Sentinel-1 multi-temporal images and SRTM DEM to produce a quasi-global foreshortening mask using the Google Earth Engine (GEE), cloud-based platform. The mean value of multi-temporal Sentinel-1 images is calculated. Then a local minimum algorithm finds probable foreshortening area. Aspect and slope information from SRTM DEM are used to refine Sentinel-1 derived foreshortening mask. The proposed method is tested in British Columbia (Canada), Everest Mountain (Nepal), and Mazandaran (Iran). The results demonstrate the reliability of proposed method to detect the foreshortening area.