A Predictive Analysis Through Earth Observation Data: Interdisciplinary Remote Sensing Applications for Evaluating Prison Reforms in Tamil Nadu

In Tamil Nadu, the criminal justice system encounters persistent challenges such as prison overcrowding, high recidivism rates, and the welfare of incarcerated individuals. Effective prison reforms are essential for mitigating these issues, but their success hinges on the meticulous monitoring and analysis of their impacts. This paper introduces an innovative approach utilizing earth science data for deep learning and remote sensing technologies for the predictive analysis of prison reforms in the region. By harnessing high-resolution satellite imagery alongside socio-economic datasets, this study develops a hybrid analytical model that integrates graph neural networks (GNNs) with gated recurrent units (GRUs). This model is designed to detect patterns and forecast the outcomes of diverse reform initiatives. Advanced techniques such as data preprocessing, feature extraction via deep convolutional generative adversarial networks (DCGANs), and data enrichment through variational autoencoders (VAEs) were applied to refine the dataset’s quality and robustness. Achieving an accuracy of 97%, the hybrid model surpasses conventional models in precision, recall, and F1 score, offering critical insights that could guide policymakers and stakeholders in enhancing the effectiveness of rehabilitation strategies and overall prison reform measures.