Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world. We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery. Such imagery can be cheaply collected and more frequently updated compared to traditional surveying methods, while containing plausibly relevant information for a range of livelihood indicators. We propose two approaches to learn from the street-level imagery: (1) a method that creates multi-household cluster representations by detecting informative objects and (2) a graph-based approach that captures the relationships between images. By visualizing what features are important to a model and how they are used, we can help end-user organizations understand the models and offer an alternate approach for index estimation that uses cheaply obtained roadway features. By comparing our results against ground data collected in nationally-representative household surveys, we demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health and its scalability by testing in two different countries, India and Kenya. Our code is available at https://github.com/sustainlab-group/mapillarygcn.

The resilience of a power system refers to its ability to resist and recover from multiple physical failures under extreme operation conditions. In this paper, we study the power grid resilience considering different time scales of recovery strategies and propose a method for identifying critical elements whose physical damage can significantly degrade the resilience of a power system. The maximum power supply (MPS) of the remaining network containing the elements that are not physically damaged is obtained and used to indicate the resilience performance of the system. We identify the critical elements in the grid by iteratively selecting and removing the link with the lowest MPS. We do simulations in the IEEE 118 Bus case to evaluate the grid resilience under various extremes and test the proposed strategy. Simulation results validate the efficacy of our proposed method in the identification of critical elements.