Abstract

The progress made in computer vision and satellite technology has opened up new possibilities for observing societies and infrastructure. Powered by a lengthening historical record of high-resolution satellite imagery and an ever-growing set of labels for training machine learning models, it is becoming feasible to investigate a decade of changes in built infrastructure and landscapes. These analyses can assist decision-makers with valuable insights into population shifts, economic trends, and infrastructure performance. Nevertheless, challenges inherent with this kind of imagery — such as varying image quality, imbalances in data collection between urban and rural areas, high costs, and the absence of image metadata — can impede the use and efficacy of these methods.

Jay Taneja is an Assistant Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst. As the lead of the STIMA Lab, he and his team develop and study applications of sensing and communications technology on the measurement and planning of societal-scale infrastructure systems in developing regions. He also leads the e-GUIDE Initiative, a multi-university consortium that collaborates with partners across sub-Saharan Africa with an aim to transform the approaches used for planning and operations of electricity infrastructure in the region. Prior to joining UMass, he was a Research Scientist leading the Energy team at the IBM Research – Africa lab in Nairobi, Kenya, from 2013 to 2016. His website is www.jaytaneja.com.