AI and Machine Learning in Geospatial Analysis: Advances, Biases, and Future Directions (2020–2026)

Abstract

The rapid integration of artificial intelligence and machine learning into geospatial analysis (2020–2026) has transformed spatial research through cloud computing, advanced deep learning architectures, and foundation models. Yet, technical progress frequently outpaces critical evaluation of spatial validity, equity, and reproducibility. This review synthesizes contemporary GeoAI developments using a PRISMA-guided scoping framework and critical narrative analysis. We examine paradigm shifts from rule-based statistics to self-supervised learning, map domain-specific applications across environmental, urban, and human geography, and interrogate systemic challenges including spatial sampling bias, algorithmic opacity, and commercial data monopolies. Findings reveal that while predictive accuracy has improved dramatically, models often fail to capture place-based context, scale dependencies, and socio-political data realities. We advocate for standardized geospatial benchmarks, transparent validation protocols, hybrid process-AI frameworks, and participatory governance to mitigate geographic inequities. Ultimately, responsible GeoAI requires sustained interdisciplinary collaboration that aligns computational innovation with rigorous spatial theory, ethical data stewardship, and actionable policy implementation globally.