To advance this vision, we highlight the potential of Geospatial Artificial Intelligence (GeoAI) and High-Performance Computing

The gap

Evidence profile

Sourced from the abstract and conclusions and future work and inline gaps of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 4 representative gaps

• Controlled environment agriculture as urban infrastructure: advancing a public agenda for spatial equity (2026) · Urban Informatics · doi

  To advance this vision, we highlight the potential of Geospatial Artificial Intelligence (GeoAI) and High-Performance Computing (HPC) as underexplored tools to enable adaptive crop management, site-specific decision support, and identify underserved communities for targeted interventions.

  generalabstractevidence 5/5
  Keywords: advance vision highlight potential geospatial artificial intelligence geoai high performance computing underexplored tools enable adaptive
• The Use of Geospatial Artificial Intelligence Technologies (GeoAI) within National Mapping Agencies: A Review (2026) · The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences · doi

  For national mapping agencies, the implementation of GeoAI is not limited to the separate technological, regulatory, ethical or development of digital capacity aspects, but involves the establishment of a global and integrated vision combining these different aspects to strengthen a sustainable and thoughtful digital sovereignty.

  generalconclusionsevidence 5/5
  Keywords: digital aspects national mapping agencies implementation geoai limited separate technological regulatory ethical development capacity involves
• GeoAI: Applications, Challenges and Future Prospects in Geography (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  for collaboration and careful application. The future of GeoAI is closely related to developments in technology and data availability. The convergence of AI and connected devices, drones, and cloud computing will facilitate real- time data processing and analysis that would increase transparency and confidence in the AI models, making them more applicable to decision-making. In this way, Geography’s future is orienting towards being more pragmatic, directed by technology, and connected to ‘real- world’ problems. It is no longer merely about learning maps or mountain shapes; Geography is now informing us and supporting us to deal better with troublesome issues like climate change, urbanization, or natural calamities. Using AI and Google Earth Engine, geographers can investigate the globe ‘live’ and can make better estimates of the situations. This will make planning and control

  generalfuture workevidence 5/5
  Keywords: future technology connected real making geography better make collaboration careful application geoai closely related developments
• Theoretical foundations and methodological advancements in geospatial big data analytics and challenges (2026) · Discover Sustainability · doi

  Therefore, future research will focus on developing and employing explainable GeoAI models that enhance and balance the predictive accuracy of the model as well as the interpretability of the model results that enable stakeholders to easily understand and apply for decision-making and scientific applications [78, 85]. Generally, future research should focus on Fentie and Minale Discover Sustainability (2026) 7:1377 Page 24 of 28 • Application of quantum computing for faster processing of large datasets, optimization problems, and hyperspectral image analysis. Overall, future research will focus on developing scalable, efficient, transparent, and collaborative geospatial technologies for real-time Earth system monitoring and management. Spatiotemporal data mining: a survey on challenges and open problems.

  generalinline gapsevidence 5/5
  Keywords: future focus developing model problems employing explainable geoai models enhance balance predictive accuracy well interpretability

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