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Time-series of satellite images may reveal important data about changes inenvironmental conditions and natural or urban landscape structures that are ofpotential interest to citizens, historians, or policymakers. We applied a fastmethod of image analysis using Self Organized Maps (SOM) and, morespecifically, the quantization error (QE), for the visualization of criticalchanges in satellite images of Las Vegas, generated across the years 1984-2008,a period of major restructuration of the urban landscape. As shown in ourprevious work, the QE from the SOM output is a reliable measure of variabilityin local image contents. In the present work, we use statistical trend analysisto show how the QE from SOM run on specific geographic regions of interestextracted from satellite images can be exploited to detect both the magnitudeand the direction of structural change across time at a glance. Significantlycorrelated demographic data for the same reference time period are highlighted.The approach is fast and reliable, and can be implemented for the rapiddetection of potentially critical changes in time series of large bodies ofimage data.

Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years