Applying Machine Learning to Earth Observations In A Standards Based Workflow.

Earth Observations (EO) enable scientific research, such as the study of meteorology and climate, ecosystems and forests, hydrology and marine life. Applications of EO help protect populations from disasters and improve life in intelligent cities. Increasingly, Machine Learning techniques are seen as key to solve these complex multidisciplinary problems. The scale and dimensionality of data involved often require the definition of processing chains, or workflows. Standards can facilitate the composition, sharing, execution and discovery of these workflows and applications, making them more useful. This paper presents three applications based on Deep Learning: a tree species classifier, a car detector and a flood detector. These applications rely on software containers to package ML framework and algorithms, as well as on workflows to process EO data. We found that these practices allow improved reuse and deployment of research assets in infrastructures. We also note the strong discriminative capabilities of Deep Learning on smaller datasets and the difficulty of generalization to other methods of sensing or regions of interest.