Future Trends in Explainable AI for Geospatial Applications

This chapter explores the transformative role of geospatial analysis and Explainable Artificial Intelligence (XAI) in addressing real-world challenges across diverse domains such as urban planning, transportation, agriculture, environmental monitoring, public health, disaster management, and defense. Geospatial tools have become integral to identifying, mapping, and analyzing Earth’s features, enabling informed decision-making through spatial visualization and modeling. The increasing integration of AI into geospatial systems introduces new opportunities for automation and prediction but also raises concerns about transparency and interpretability. To address this, the chapter emphasizes the emerging importance of XAI in geospatial applications, highlighting how hybrid models, causal inference techniques, visual explanation tools, and uncertainty quantification can enhance the trust and reliability of AI-driven insights. Future trends such as edge computing, federated learning, and human-AI collaboration are examined for their potential to create interpretable, privacy-preserving, and real-time geospatial systems. Through practical insights and theoretical foundations, this chapter provides a forward-looking perspective on building transparent, accountable, and sustainable geospatial intelligence systems powered by explainable AI.