Machine learning in geosciences and remote sensing

By David John Lary, Amir Hossein Alavi, Amir H. Gandomi, A. Walker. 2016

982 citations recorded by the source corpus.

Abstract Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.) that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/32e29041fa352a9df0889f42807ed6141bc0b5ff

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Citations in the corpus

In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness (2020, background): “Developing machine learning models trained on remote sensing data is currently a popular line of work for practical problems such as typhoon rainfall estimation, monitoring reservoir water quality, and soil moisture estimation [29, 32, 1].”
Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction (2019, methodology): “Predictive modelling is a method commonly used to derive spatially continuous datasets from limited ﬁeld data (e.g. Lary et al., 2016).”
The Future of Earth Observation in Hydrology. (2017, positive): “…little to no knowledge of the physical processes underlying the observed variables is required to implement them, which releases their potential for discovering unexpected relationships as new hydrological and climatic observations become available (Faghmous and Kumar, 2014; Lary et al., 2016).”
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing (2017, methodology): “…et al., 2015), spectral matching techniques (Dheeravath et al., 2010), support vector machines (Mountrakis et al., 2011), random forest algorithm (Tatsumi et al., 2015) and a number of other machine learning algorithms (DeFries, 2000; Duro et al., 2012; Lary et al., 2016; Pantazi et al., 2016).”
Tackling Climate Change with Machine Learning (2019, background): “These include black box problems, for instance sensor calibration [474], and classification of observational data, for instance classifying crop cover or identifying pollutant sources in satellite imagery [475, 476].”