  Remotely sensed nighttime lights (NTL) uniquely capture urban change
processes that are important to human and ecological well-being, such as
urbanization, socio-political conflicts and displacement, impacts from
disasters, holidays, and changes in daily human patterns of movement. Though
several NTL products are global in extent, intrinsic city-specific factors that
affect lighting, such as development levels, and social, economic, and cultural
characteristics, are unique to each city, making the urban processes embedded
in NTL signatures difficult to characterize, and limiting the scalability of
urban change analyses. In this study, we propose a data-driven approach to
detect urban changes from daily satellite-derived NTL data records that is
adaptive across cities and effective at learning city-specific temporal
patterns. The proposed method learns to forecast NTL signatures from past data
records using neural networks and allows the use of large volumes of unlabeled
data, eliminating annotation effort. Urban changes are detected based on
deviations of observed NTL from model forecasts using an anomaly detection
approach. Comparing model forecasts with observed NTL also allows identifying
the direction of change (positive or negative) and monitoring change severity
for tracking recovery. In operationalizing the model, we consider ten urban
areas from diverse geographic regions with dynamic NTL time-series and
demonstrate the generalizability of the approach for detecting the change
processes with different drivers and rates occurring within these urban areas
based on NTL deviation. This scalable approach for monitoring changes from
daily remote sensing observations efficiently utilizes large data volumes to
support continuous monitoring and decision making.
