We mention also the “conserved” data of photographic astroplates accumulated over the centuries, which have been actively digitized during the last decades and provide a new knowledge from the comparative analysis of old and new observational material.Īstronomy research is changing from being hypothesis-driven to being data-driven to being data-intensive. The chapter describes briefly visual, photographic, and CCD surveys of stars, galaxies and the intergalactic medium, spectral photographic and spectral CCD surveys, and multiwavelength ground-based and space-born databases and archives, which made it possible to create a high-precision coordinate system, to discover new properties of celestial bodies, and, as a result, to construct three-dimensional models of the visible parts of the universe. Thanks to this, astronomy has moved from the study of individual objects to the study of the universe as a whole and has become the science of Big Data. This chapter traces the development of astronomical observational methods from visual, for decades tracking the behavior of individual objects, to modern space missions that produce data simultaneously for several hundred thousand objects in different spectral ranges. The chapter concludes with a summary of some of the key research issues in ML related to astronomy and geosciences, with emphasis on the scope for the application of ML algorithms to the rapidly increasing volumes of astronomical and remotely sensed geophysical data for geological mapping and other problems.
Selected case study applications in which ML techniques have been successfully deployed in astronomy and geosciences are described. Scalable ML algorithms and frameworks are also described. Current trends and recent developments in ML algorithms are discussed. ML algorithms are programs of data-driven inference tools that offer an automated means of recognizing patterns in high-dimensional data.
The supervised, unsupervised, semisupervised and reinforcement learning types are described. Special attention is given to inductive learning, which is among the most mature of the ML approaches currently available. This chapter introduces and evaluates several ML techniques. While the adoption of ML methods in astronomy and geosciences has been slow, there are several published studies using ML in these disciplines. Astronomy and geosciences are two areas where the application of ML can be very fruitful. ML is concerned with enabling computer programs automatically to improve their performance at some tasks through experience. Machine learning (ML) is a subset of artificial intelligence that develops dynamic algorithms capable of data-driven decisions, in contrast to models that follow static programming instructions.
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