Thanks to the digital, mobile and sensor revolutions, massive amounts of data are becoming available at unprecedented spatial, temporal, and thematic scales. This leads to the practical problem of transforming big geodatasets into actionable information that can support a variety of decision-making processes. In this respect, geodata science workflows are not only key to processing big geospatial datasets but also to sharing the extracted information and knowledge and to ensuring the reproducibility of the results.
To handle and analyse massive and potentially heterogeneous amounts of spatio-temporal data, scientists need to 1) understand the particular characteristics of big geodata, 2) learn to work with scalable data management and processing systems, and 3) develop scalable and robust data mining and machine learning workflows. Hence, this course presents theories, methods, and techniques to build scalable solutions for handling and analysing big geodata.