Geoinformatics for Global Land Analysis and Discovery (GLAD) with Artificial Intelligence (AI)

Abstract

The convergence of geographic information systems (GIS), artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and the Global Land Analysis and Discovery (GLAD) framework is redefining modern geospatial intelligence. This study presents an integrated workflow that begins with multi-source geospatial data acquisition—including satellite imagery, IoT sensor streams, and large-scale data archives—and applies AI-driven deep learning models and GLAD-based change‑detection techniques to generate high-frequency environmental and urban insights. These processed outputs are incorporated into a GIS environment for spatial mapping, real-time analytics, and automated alert generation, enabling decision support across environmental monitoring, smart-city traffic optimization, disaster-response coordination, and public-health surveillance. Case studies such as Singapore’s intelligent traffic management system and GLAD-enabled deforestation monitoring demonstrate measurable impacts, including improved detection accuracy, reduced travel time, lower emissions, and faster operational decision-making. Looking ahead, advancements such as edge-enabled real-time GIS, quantum-accelerated predictive modeling, and immersive AR/VR spatial interfaces are expected to further enhance system responsiveness and analytical depth. By synthesizing current capabilities with emerging innovations, this study underscores the transformative role of GIS-smart technology integration in building efficient, sustainable, and resilient geospatial intelligence systems.