The evolution of data acquisition systems has enabled increasing data quality and the volume of information to be analyzed, processed and interpreted. Nowadays, there has been a growing interest in the geophysical time series and image processing analysis in all most disciplines relates to Earth Sciences, such as Seismology, Geochemistry, Geodesy, Volcanology, Geology and Satellite Observations.
A major challenge that arises is how to structure and organize the huge amount of data and to determine the type of information that could help the scientific community for a deeper knowledge on the complex dynamics of geophysical and geochemical systems in our Planet.
The high volume of data recorded by those systems requires an appropriate framework that can enhance classical approaches by exploiting the latent knowledge embedded in the data. New rising methodologies have to tackle the long-term problems of data management, accessibility and deployment. Data Mining, Cloud Computing and Machine Learning are the most appropriate disciplines for the analysis of such high throughput data.
In this session, we welcome contributions focused on recent machine learning advances applied to Earth and planetary sciences, with a focus on remote sensing techniques and early warning systems. This is a highly interdisciplinary session, where artificial intelligence is combined with Earth sciences techniques in order to improve the knowledge of the complexity of our Planet.
This session is partially sponsored by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 798480, and by Government of Spain through the research project TEC2015-814 68752.