  In this work, we present a methodology for monitoring man-made,
construction-like activities in low-resolution SAR imagery. Our source of data
is the European Space Agency Sentinel-l satellite which provides global
coverage at a 12-day revisit rate. Despite limitations in resolution, our
methodology enables us to monitor activity levels (i.e. presence of vehicles,
equipment) of a pre-defined location by analyzing the texture of detected SAR
imagery. Using an exploratory dataset, we trained a support vector machine
(SVM), a random binary forest, and a fully-connected neural network for
classification. We use Haralick texture features in the VV and VH polarization
channels as the input features to our classifiers. Each classifier showed
promising results in being able to distinguish between two possible types of
construction-site activity levels. This paper documents a case study that is
centered around monitoring the construction process for oil and gas fracking
wells.
