Converging high-performance computing and machine learning for geospatial discovery and innovation

The synthesis of high-performance computing (HPC) and machine learning has been critical for addressing complex geospatial problems and enabling geospatial knowledge discovery. This paper conducts a systematic review of 289 selected literature indexed in the Web of Science Core Collection from 1996 to 2024 that integrates HPC and machine learning (ML) for geospatial discovery and innovation. Starting in 2015, there has been a significant increase in studies combining HPC including supercomputing, parallel computing, cloud computing and fog computing with machine learning models for geospatial research across domains. This paper categorizes prior work based on the purposes of leveraging machine learning and HPC for geospatial knowledge discovery including speedup, accuracy improvement, spatial and temporal resolution improvement, scaling up, real-time analysis and novel model development. In addition, we propose a future research agenda including five key research questions focusing on scaling geospatial foundation models on HPC systems, integrating ML with physics-based models while preserving fidelity, quantifying uncertainty and ethical risks in ML predictions, balancing computational intensity with energy efficiency and ensuring HPC-ML pipelines are FAIR and cross-sector usable as well as four interconnected research thrusts: scalable geospatial data fabrics, geospatial foundation models, domain knowledge and ML integration, and responsible and transparent geospatial AI, along with their future implementation strategies and anticipated impacts.