Over the past decade, since the publication of the first edition, there have been new advances in solving complex geoinformatics problems. Advancements in computing power, computing platforms, mathematical models, statistical models, geospatial algorithms, and the availability of data in various domains, among other things, have aided in the automation of complex real-world tasks and decision-making that inherently rely on geospatial data. Of the many fields benefiting from these latest advancements, Machine Learning, particularly Deep Learning, virtual reality, and game engine, have increasingly gained the interest of many researchers and practitioners. This revised new edition provides up-to-date knowledge on the latest developments related to these three fields for solving geoinformatics problems. Geospatial Big Data present two opportunities for the increased use of Machine Learning in the geospatial analytics domain. First, geospatial Big Data have created a shift toward considering large amounts of data as a resource that can be used to add value to an organization. Second, by virtue of the three Vs, volume, velocity, and variety, of Big Data, there is a shift away from complex models that require extensive computational and memory resources to techniques that instead can produce results in a more computationally efficient manner.