Geospatial AI for Climate Change Mitigation and Urban Resilience

Climate change poses unprecedented challenges to urban environments, demanding innovative solutions for mitigation and resilience. Traditional geospatial methods provide valuable insights into spatial patterns of climate impacts, yet they often lack the predictive power and scalability required for dynamic urban systems. This paper explores the integration of Artificial Intelligence (AI) with geospatial technologies to address climate change mitigation and strengthen urban resilience. By leveraging satellite imagery, remote sensing data, and Geographic Information Systems (GIS), coupled with machine learning and deep learning models, we present an AI-driven framework for monitoring, predicting, and managing climate-related risks. The study emphasizes applications such as urban heat island analysis, flood risk prediction, air quality assessment, and carbon emission monitoring. The proposed approach enhances the ability of policymakers, planners, and urban managers to design sustainable, adaptive strategies for cities facing increasing environmental stress. Findings highlight the transformative role of Geospatial AI in enabling data-driven decision-making, optimizing resource allocation, and fostering climate-resilient urban development.