Satellite-based Earth observation plays a critical role in oceanography, climate monitoring, disaster management, and environmental analysis. Over the years, several platforms such as MOSDAC Live, Zoom Earth, NASA Live Earth, Nullschool, and similar systems have been developed to visualize satellite-derived data and monitor Earth's dynamic processes. While these platforms provide valuable real-time or near real- time visualization capabilities, they largely rely on traditional visualization approaches and offer limited interactivity, predictive intelligence, and user-centric analytical features. Moreover, the complexity of scientific data often restricts effective understand- ing by non-technical users and decision-makers. This paper presents a conceptual and review-based study of existing satellite-based Earth observation and data visualiza- tion platforms, with a focus on identifying their technological limitations in terms of real-time data integration, intelligent analytics, scalability, and user accessibility. Based on this anal- ysis, the paper proposes a futuristic software framework for oceanographic and Earth monitoring applications that integrates artificial intelligence and machine learning, real-time satellite data processing, smart analytical dashboards, and interactive visualization techniques. The proposed framework emphasizes automated data updates, predictive insights, cross-domain data fusion, and an AI-assisted conversational interface to enhance both scientific analysis and public understanding. The proposed approach aims to transform conventional Earth observation systems into intelligent, user-friendly, and decision- support platforms capable of supporting scientists, researchers, and non-technical stakeholders alike. This research highlights the potential of advanced visualization, AI-driven analytics, and cloud-based architectures in shaping the future of satellite-based oceanographic monitoring and Earth observation systems.
Singh et al. (Thu,) studied this question.