A review of cyberinfrastructure for machine learning and big data in the geosciences

Abstract

The use of artificial intelligence (AI) and machine learning (ML) methods in the geosciences can be categorized into three types, those that: (1) accelerate computationally expensive Earth system models; (2) fill the vacuum where numerical and physics-based models struggle; and (3) enable and enlighten data-driven discoveries. To achieve these tasks, many cyberinfrastructure (CI) systems are required. This chapter reviews the cutting-edge CI aiding the implementation of AI in the geosciences. Each technique presented is evaluated to assist geoscientists in determining how appropriate it is. Use cases in the subdomains of seismology, hydrology, and climatology are introduced to help readers understand the workflows. Challenges and future opportunities for CI development center on big data, provenance, interoperability, and heterogeneity due to the scale and complexity that future AI models in the geosciences will require.

A Review of Practical AI for Remote Sensing in Earth Sciences

“…The remote sensing community has extensively utilized conventional machine learning methods for various tasks such as classification, object detection, and geophysical parameter estimation. These methods have proven effective in handling multi-temporal and multi-sensor remote sensing data, providing valuable information for environmental monitoring [14,[50][51][52][53].…”