This chapter examines the three foundational pillars—data, algorithms, and computational infrastructure—that support the emergence and growth of geospatial artificial intelligence (GeoAI) in human geography. We trace their historical evolution, from the era of quantitative geography and early computational methods to the recent surge in high-performance computing and advanced machine-learning models. Drawing on examples such as street-view image analysis for urban built environment auditing, we illustrate how these pillars enable more efficient and nuanced spatial analyses and decision-making processes. While GeoAI tools offer powerful means to uncover previously hidden patterns and improve our understanding of socio-spatial phenomena, they also prompt critical reflection. As GeoAI increasingly engages with sensitive issues related to bias, privacy, representational fairness, and the environmental costs of training large models, human geographers are examining its theoretical and ethical implications through critical perspectives and social theories. Recognizing the potential synergies—and the tensions—between innovative computational methods and critical inquiry fosters a more inclusive, responsible, and socially engaged framework for integrating GeoAI into human geography research and practice.