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

TL;DR

This paper presents a fast, reliable method using Self Organized Maps and quantization error to detect and visualize structural changes in satellite images of Las Vegas over 24 years, aiding policymakers and researchers.

Contribution

The study introduces a novel application of SOM and QE for rapid detection of environmental and urban landscape changes in satellite image time series.

Findings

QE correlates with demographic changes

Method detects structural change magnitude and direction

Approach is fast and suitable for large datasets

Abstract

Time-series of satellite images may reveal important data about changes in environmental conditions and natural or urban landscape structures that are of potential interest to citizens, historians, or policymakers. We applied a fast method of image analysis using Self Organized Maps (SOM) and, more specifically, the quantization error (QE), for the visualization of critical changes in satellite images of Las Vegas, generated across the years 1984-2008, a period of major restructuration of the urban landscape. As shown in our previous work, the QE from the SOM output is a reliable measure of variability in local image contents. In the present work, we use statistical trend analysis to show how the QE from SOM run on specific geographic regions of interest extracted from satellite images can be exploited to detect both the magnitude and the direction of structural change across time at a glance. Significantly correlated demographic data for the same reference time period are highlighted. The approach is fast and reliable, and can be implemented for the rapid detection of potentially critical changes in time series of large bodies of image data.