  We present an algorithm capable of identifying a wide variety of
human-induced change on the surface of the planet by analyzing matches between
local features in time-sequenced remote sensing imagery. We evaluate feature
sets, match protocols, and the statistical modeling of feature matches. With
application of KAZE features, k-nearest-neighbor descriptor matching, and
geometric proximity and bi-directional match consistency checks, average match
rates increase more than two-fold over the previous standard. In testing our
platform, we developed a small, labeled benchmark dataset expressing
large-scale residential, industrial, and civic construction, along with null
instances, in California between the years 2010 and 2012. On the benchmark set,
our algorithm makes precise, accurate change proposals on two-thirds of scenes.
Further, the detection threshold can be tuned so that all or almost all
proposed detections are true positives.
