Production Machine Learning Frameworks for Geospatial Big Data

We explore the use of production Machine Learning (ML) frameworks for automatically building ML models for cloud-based services that exploit geospatial big data and value-added products. We combine two widely used production ML frameworks to hierarchically decompose the tasks involved with the fetching and preprocessing of the data as well as with model training, evaluation, and selection. We assess the usability, reproducibility and performance of the frameworks both qualitatively and quantitatively. We examine the challenging case of a cloud-based seabed mapping service that process multispecrtal multibeam echosounder data captured in different marine surveys, involving a number of data processing and machine learning tasks.