International Journal of Geographical Information Science

Critical Challenges in GeoAI

Geospatial Artificial Intelligence (GeoAI) has rapidly emerged as a transformative force in geographic information science, enabling breakthroughs in environmental monitoring, spatial prediction, urban analytics, disaster response, and Earth system modeling. The integration of deep learning, foundation models, large language models, and multimodal AI systems into geospatial workflows has generated significant excitement and accelerated methodological innovation.

Despite this progress, fundamental questions remain insufficiently examined. The rapid adoption of GeoAI has, in some cases, outpaced careful reflection on its theoretical foundations, methodological limits, epistemological implications, environmental costs, and long-term role within spatial science. As GeoAI systems grow in scale and influence, there is an urgent need to critically assess what GeoAI has achieved, where it falls short, and how it should evolve to advance scientific discovery and responsible decision-making.

This special issue addresses the critical challenges and major opportunities defining the next generation of GeoAI. Moving beyond incremental technical improvements, we seek contributions that interrogate the conceptual, methodological, societal, and environmental foundations of the field. We invite authors to propose innovative solutions to major hurdles and chart high-value future directions for GeoAI.

We welcome manuscripts that examine, but are not limited to, the following themes:

• Theoretical foundations of GeoAI and its relationship to spatial analysis, spatial statistics, and geographic theory
• Novel methodological frameworks or breakthrough application domains that define the future trajectory of GeoAI
• Strengthening of AI breakthroughs (e.g., reasoning-capable AI) to honor "laws" of geography
• Spatialization of AI concepts, e.g., spatial transfer learning, spatial attention and transformers, spatial representation learning, spatial inductive bias, spatial diffusion models, and spatial neurosymbolic reasoning
• The role of foundation models, neural-network based world models, and large-scale pretrained models in spatial sciences
• Traditional and spatial uncertainty quantification, model robustness, and misuse risks in GeoAI applications
• Environmental, economic, and societal implications of large-scale GeoAI systems including (hyperscaler) data centers
• Educational challenges, curriculum transformation and workforce development in the age of GeoAI
• GeoAI for national priorities: accelerating science and the Genesis mission

We particularly encourage contributions that not only reflect on limitations but also advance constructive pathways forward, evaluation paradigms, hybrid modeling strategies, and interdisciplinary approaches that strengthen the scientific and societal foundations of GeoAI. Other issues such as ethics, reproducibility, transparency, generalizability, and biases, while presenting critical challenges in GeoAI, are addressed in existing IJGIS special issues. Submissions primarily focused on these topics are encouraged to consider those venues. By bringing together foundational reflection and forward-looking innovation, this special issue aims to shape a more rigorous, responsible, and impactful future for GeoAI within GIScience and beyond.