Automated learning has recently found many applications in the aerospace and remote sensing sector. These applications range from bias correction to recovery algorithms, from acceleration of code to detection of disease in crops. As a broad subfield of artificial intelligence, automatic learning refers to algorithms and techniques that allow computers to "learn". The main focus of automatic learning is to extract information from the data automatically using computational and statistical methods.
During the last decade, considerable progress has been made in the development of a machine learning methodology for a variety of Earth applications including trace gases, recoveries, aerosol products, land surface products, vegetation indices and, more Recently, oceanic 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 these works have even received special recognition as a highlight of NASA's Aura Science (Lary et al., 2007) and the commendation of NASA's MODIS instrument kit (Lary et al., 2009). The two types of machine learning algorithms typically used are neural networks and vector support 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.