  Due to insufficient or difficult to obtain data on development in
inaccessible regions, remote sensing data is an important tool for interested
stakeholders to collect information on economic growth. To date, no studies
have utilized deep learning to estimate industrial growth at the level of
individual sites. In this study, we harness high-resolution panchromatic
imagery to estimate development over time at 419 industrial sites in the
People's Republic of China using a multi-tier computer vision framework. We
present two methods for approximating development: (1) structural area coverage
estimated through a Mask R-CNN segmentation algorithm, and (2) imputing
development directly with visible & infrared radiance from the Visible Infrared
Imaging Radiometer Suite (VIIRS). Labels generated from these methods are
comparatively evaluated and tested. On a dataset of 2,078 50 cm resolution
images spanning 19 years, the results indicate that two dimensions of
industrial development can be estimated using high-resolution daytime imagery,
including (a) the total square meters of industrial development (average error
of 0.021 $\textrm{km}^2$), and (b) the radiance of lights (average error of 9.8
$\mathrm{\frac{nW}{cm^{2}sr}}$). Trend analysis of the techniques reveal
estimates from a Mask R-CNN-labeled CNN-LSTM track ground truth measurements
most closely. The Mask R-CNN estimates positive growth at every site from the
oldest image to the most recent, with an average change of 4,084
$\textrm{m}^2$.
