Scalable AI Models for Climate Change Mitigation Using Multisource Geospatial Big Data

Abstract

This study discusses how AI can be combined with multi-source geospatial big data. It improves predictions and advisory activities to combat climate change at regional and global levels. The issue of climate change is a burning world problem that requires technological breakthroughs. The phenomenon of access to geospatial big data by satellites, sensors, and drones. Climate monitoring systems are giving an unprecedented chance to know environmental dynamics. Scalable models of artificial intelligence learn meaningful dissimilarities amid rich and large-scale data sources. This paper uses a multi-level AI architecture using deep learning, ensemble, and reinforcement learning methodology. The data sources are used, which include NASA Earth data, Copernicus, and national meteorological data. Preprocessing involves spatial harmonization, feature engineering, and noise reduction. AI models are applied using a distributed computing infrastructure on cloud-based platforms over scalable models. The parameters used to examine model performance are accuracy and scalability. The offered scalable AI models have high potential in the mitigation of climate change. This analyzes and predicts on a major scale its environmental patterns based on multisource geospatial data. Findings indicate improved precision in forecasting priority zones to be intervened with and aid the real-time judgment process by the policymakers and the green organizations.