Automating image classification by using multi-resolution spaceborne imagery, cloud-based machine learning algorithms and open-source software and data

This paper describes the extraction of critical terrestrial features from multispectral (MSS) spaceborne imagery. We present a methodology to extract terrestrial features from spatiotemporal datasets by using machine learning algorithms, open-source software, and datasets. We used the Amazon Web Services sage-maker framework to conduct full data life cycle (FDLC) projects and automate steps from data acquisition to data mining and visualization. The results demonstrate a robust and efficient model to conduct image analyses with spatio-temporal datasets to extract variables that may be useful for a wide range of applications. The results have significant implications to analyze time-series of spaceborne imagery for both near-real time and other applications that are driven by data from the archives.