Machine Learning (ML) refers to a set of data-driven algorithms and techniques that automate the prediction, classification, and clustering of data. Machine learning can play a critical role in spatial problem solving in a wide range of application areas, from image classification to spatial pattern detection to multivariate prediction.
In addition to traditional Machine Learning techniques, ArcGIS also has a subset of ML techniques that are inherently spatial. These spatial methods that incorporate some notion of geography directly into their computation can lead to deeper understanding. The spatial component often takes the form of some measure of shape, density, contiguity, spatial distribution, or proximity. Both traditional and inherently spatial machine learning can play an important role in solving spatial problems, and ArcGIS supports their use in a number of ways.
Machine learning can be computationally intensive and often involves large and complex data. Esri’s continued advancements in data storage and both parallel and distributed computing make solving problems at the intersection of ML and GIS increasingly possible.
Learn some examples from this ArcGIS blog: Machine Learning in ArcGIS | ArcGIS Blog
