Satellite imagery often suffers from limited spatial resolution and, in many cases, high acquisition costs. These factors restrict their use in applications such as urban monitoring, land management, and wildlife studies. This work proposes an AI-based super-resolution approach that leverages high resolution aerial imagery to train a Generative Adversarial Network. Specifically, the ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) architecture is adapted and trained using aerial orthophotos, enabling the transfer of learned spatial representations to low-resolution satellite images. The trained model is evaluated on satellite image patches at 2 and 4 super-resolution scales. Performance is assessed using structural, perceptual, and chromatic metrics, including SSIMY, MS-SSIM, LPIPS and CIEDE2000. The results show clear improvements, with increased sharpness, enhanced edge definition, and consistent reconstruction of urban structures and terrain features. From a quantitative perspective, the 2 scale achieves the best overall metric values, while the 4 scale maintains stable and meaningful performance despite the higher reconstruction difficulty. These findings demonstrate the feasibility of transferring super-resolution capabilities from aerial images to satellite imagery, even in the presence of spectral and geometric differences between acquisition domains. Overall, this study provides a solid foundation for the development of low-cost, AI-driven satellite image super-resolution models and outlines future research directions focused on dataset expansion, domain adaptation strategies, and sensor-specific architectural improvements.
Magda Alexandra Trujillo-Jiménez, Francisco Iaconis, Debora Pollicelli et al.· IEEE Latin America Transacti...· 0 citations
The rapid onset of the Fourth Industrial Revolution and increasing global environmental volatility have necessitated a fundamental reimagining of national educational frameworks. This study examines the systemic paradigm shift represented by the Malaysia New Curriculum 2027, a landmark reform aimed at moving the nation from a traditional, performance-centric model to a fluid, competency-based ecosystem. Using an Integrative Conceptual Synthesis methodology, the research triangulates Malaysian national policy foundations with authoritative global benchmarks, including the OECD Learning Compass 2030 and UNESCO’s ESD 2030 roadmap, to bridge the persistent gap between theoretical policy aspirations and practical classroom realities. The analysis identifies critical systemic tensions currently hindering national progress, specifically the “content-density paradox” and the “readiness deficit” regarding digital ethics in the emerging era of generative AI. The primary finding is the proposal of the Sustainable-Holistic Synergy Model (SHSM), which posits that genuine future-readiness is a synergistic outcome of three interdependent pillars: adaptive cognitive flexibility, ethical digital humanism, and stewardship-driven sustainability. The study concludes that the ultimate success of the 2027 reform depends on a radical redefinition of national assessment tools and the empowerment of teachers as facilitators of synergy. Future research should empirically validate these SHSM pillars through longitudinal pilot testing across Malaysia’s diverse urban and rural school landscapes to ensure an equitable and inclusive national transformation
Mohd Lokman Abdullah, A. S. M. Azizan· Muallim Journal of Social Sc...· 0 citations
The Adaptive Repayment Optimisation Engine is introduced, a novel framework that applies constrained stochastic optimisation to the design of loan repayment schedules for small and medium-sized enterprises (SMEs) and contributes to the operations research literature by bridging stochastic programming, explainable AI, and financial regulation in a novel application domain.
John Christiansen· Open Access Journal of Artif...· 0 citations
This paper outlines a systematic framework designed to integrate generative AI modalities into the field of restorative tattoo art, specifically targeting psychological and somatic rehabilitation post-oncological disease.
I. Ihnatenko, Liudmyla Vyshkvarok, Diana Raschupkina· Metaverse Science, Society a...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
