  Governments and international organizations the world over are investing
towards the goal of achieving universal energy access for improving
socio-economic development. However, in developing settings, monitoring
electrification efforts is typically inaccurate, infrequent, and expensive. In
this work, we develop and present techniques for high-resolution monitoring of
electrification progress at scale. Specifically, our 3 unique contributions
are: (i) identifying areas with(out) electricity access, (ii) quantifying the
extent of electrification in electrified areas (percentage/number of
electrified structures), and (iii) differentiating between customer types in
electrified regions (estimating the percentage/number of
residential/non-residential electrified structures). We combine high-resolution
50 cm daytime satellite images with Convolutional Neural Networks (CNNs) to
train a series of classification and regression models. We evaluate our models
using unique ground truth datasets on building locations, building types
(residential/non-residential), and building electrification status. Our
classification models show a 92% accuracy in identifying electrified regions,
85% accuracy in estimating percent of (low/high) electrified buildings within
the region, and 69% accuracy in differentiating between (low/high) percentage
of electrified residential buildings. Our regressions show $R^2$ scores of 78%
and 80% in estimating the number of electrified buildings and number of
residential electrified building in images respectively. We also demonstrate
the generalizability of our models in never-before-seen regions to assess their
potential for consistent and high-resolution measurements of electrification in
emerging economies, and conclude by highlighting opportunities for improvement.
