3.3 Preprocessing of Layers First, all raster layers were spatially aligned and resampled to a common grid with a spatial resolution of 100 m. This step ensured pixel-wise correspondence across all input features. Missing and NoData values were addressed using a K-Nearest Neighbors (KNN) imputation approach with k = 10, where missing values were replaced using the median of neighboring pixels. Finally, all features were normalized using MinMax scaling, transforming values into a [0,1] range. 4. METHODOLOGY Figure 15 – Selected geospatial layers used to construct the input dataset for the VAE-CNN model.
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