  Novel methods of analysis are needed to help advance our understanding of the
intricate interplay between landscape changes, population dynamics, and
sustainable development. Self organized machine learning has been highly
successful in the analysis of visual data the human expert eye may not be able
to see. Thus, subtle but significant changes in fine visual detail in images
relating to trending alterations in natural or urban landscapes may remain
undetected. In the course of time, such changes may be the cause or the
consequence of measurable human impact. Capturing such change in imaging data
as early as possible can make critical information readily available to
citizens, professionals and policymakers. This promotes change awareness, and
facilitates early decision making for action. Here, we use unsupervised
Artificial Intelligence (AI) that exploits principles of self-organized
biological visual learning for the analysis of imaging time series. The
quantization error in the output of a Self Organizing Map prototype is
exploited as a computational metric of variability and change. Given the proven
sensitivity of this neural network metric to the intensity and polarity of
image pixel colour, it is shown to capture critical changes in urban
landscapes. This is achieved here on imaging data for two regions of geographic
interest in Las Vegas County, Nevada, USA. The SOM analysis is combined with
the statistical analysis of demographic data revealing human impacts. These
latter are significantly correlated with the structural change trends in the
numerical data for the specific regions of interest. By correlating data
relative to the impact of human activities with numerical data indicating
structural evolution, human footprint related environmental changes can be
predictably scaled.
