Artificial Intelligence in Aerospace

Machine learning has recently found many applications in aerospace and remote sensing. These applications range from bias correction to retrieval algorithms, from code acceleration to detection of disease in crops. As a broad subfield of artificial intelligence, machine learning is concerned with algorithms and techniques that allow computers to “learn”. The major focus of machine learning is to extract information from data automatically by computational and statistical methods. Over the last decade there has been considerable progress in developing a machine learning methodology for a variety of Earth Science applications involving trace gases, retrievals, aerosol products, land surface products, vegetation indices, and most recently, ocean products (Yi and Prybutok, 1996, Atkinson and Tatnall, 1997, Carpenter et al., 1997, Comrie, 1997, Chevallier et al., 1998, Hyyppa et al., 1998, Gardner and Dorling, 1999, Lary et al., 2004, Lary et al., 2007, Brown et al., 2008, Lary and Aulov, 2008, Caselli et al., 2009, Lary et al., 2009). Some of this work has even received special recognition as a NASA Aura Science highlight (Lary et al., 2007) and commendation from the NASA MODIS instrument team (Lary et al., 2009). The two types of machine learning algorithms typically used are neural networks and support vector machines. In this chapter, we will review some examples of how machine learning is useful for Geoscience and remote sensing, these examples come from the author’s own research.