Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas

This study presents a novel deep learning-based super-resolution framework for enhancing remote sensing imagery to assess groundwater quality and environmental conditions in Lahore, Pakistan. We developed a convolutional neural network architecture that upscales low-resolution satellite imagery to generate high-resolution (0.5 m) outputs, achieving a peak signal-to-noise ratio of 32.4 dB and structural similarity index of 0.91. The enhanced imagery enabled precise delineation of urban features and environmental parameters affecting groundwater quality. Using the super-resolved images alongside traditional water quality parameters (pH, hardness, TDS) analyzed through fuzzy analytic hierarchy process, we…