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Pré-Publication, Document De Travail Année : 2023

Self-Organizing Artificial Intelligence Captures Landscape Changes Correlated with Human Impact Data

Résumé

Self-organization is the core principle of all learning in Adaptive Resonance Theory (ART), which has been highly successful in accounting for biological visual learning, or biologically plausible computational modelling of visual processing. Such processing includes the analysis of visual data we may not be able to see consciously, such as changes in fine visual detail in images relating to alterations in natural or urban landscapes, for example. In the course of time, such imaging data may reveal structural changes that are the consequence of measurable human impact or climate change. Capturing such change in time series of satellite images before the human eye can see them makes them available at early stages to citizens, professionals, and policymakers, 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 in the example of satellite images from two regions of geographic interest in Las Vegas County, Nevada, USA across the years 1984-2008. The SOM-QE analysis is combined with the statistical analysis of demographic data revealing human impacts 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-QE analysis, as a parsimonious and reliable AI approach, to the rapid detection of human footprint-related environmental change.
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hal-04345752 , version 1 (14-12-2023)

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  • HAL Id : hal-04345752 , version 1

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John Mwangi Wandeto, Birgitta Dresp. Self-Organizing Artificial Intelligence Captures Landscape Changes Correlated with Human Impact Data. 2023. ⟨hal-04345752⟩
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