Abstract:
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, for example, may remain undetected. In the course of time, such
changes may be the cause or the consequence of measurable human impact, or climate change. Capturing such change in time series of satellite images before the human eye can detect the signs thereof makes important trend information readily available at
early stages 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 time series of satellite images. The Quantization Error (QE) 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 contrast, and its proven selectivity to pixel colour, it is shown to capture critical
changes in urban landscapes. This is achieved here on the example of satellite images from two regions of geographic interest in
Las Vegas County, Nevada, USA across the years 1984-2008. The SOM analysis is combined with the statistical analysis of demographic data revealing human impacts significantly correlated with the structural changes in the specific regions of interest. By
correlating the impact of human activities with the structural evolution of urban environments we further expand SOM analysis
as a parsimonious and reliable AI approach to the rapid detection of human footprint-related environmental change.