This session aims to bring together researchers working with big data sets generated from monitoring networks, extensive observational campaigns and detailed modeling efforts across various fields of geosciences. Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards semi or fully automated inference of relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:
• Dimensionality and complexity of big data sets
• Data mining in Earth sciences
• Machine learning, deep learning and Artificial Intelligence applications in geosciences
• Visualization and visual analytics of big and high-dimensional data
• Informatics and data science
• Emerging big data paradigms, such as datacubes

All areas in the Earth sciences face the same problem of dealing with larger and more complex data sets that need to be analyzed, visualized and understood. Depending on the application domain and the specific scientific questions to be solved, different visualization strategies and techniques have to be applied. Yet, how we communicate those complex data sets, and the effect that visualization strategies and choices have on different (expert and non-expert) audiences as well as decision-makers remains an under-researched area of interest. For this "PICO only" session, we not only invite submissions that demonstrate how to create effective and efficient visualizations for complex and large earth science data sets but also those that discuss possibilities and challenges we face in the communication and tailoring of such complex data to different users/ audiences. Submissions are encouraged from all geoscientific areas that either show best practices or state of the art in earth science data visualization or demonstrate efficient techniques that allow an intuitive interaction with large data sets. In addition, we would like to encourage studies that integrate thematic and methodological insights from fields such as for example risk communication more effectively into the visualization of complex data. Presentations will be given as PICO (Presenting Interactive COntent) on large interactive touch screens. This session is supported by ESiWACE2. ESiWACE2 has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 823988.

There are many ways in which machine learning promises to provide insight into the Earth System, and this area of research is developing at a breathtaking pace. If unsupervised, supervised as well as reinforcement learning can hold this promise remains an open question, particularly for predictions. Machine learning could help extract information from numerous Earth System data, such as satellite observations, as well as improve model fidelity through novel parameterisations or speed-ups. This session invites submissions spanning modelling and observational approaches towards providing an overview of the state-of-the-art of the application of these novel methods.

Environmental systems often span spatial and temporal scales covering different orders of magnitude. The session is oriented in collecting studies relevant to understand multiscale aspects of these systems and in proposing adequate multi-platform and inter-disciplinary surveillance networks monitoring tools systems. It is especially aimed to emphasize the interaction between environmental processes occurring at different scales. In particular, a special attention is devoted to the studies focused on the development of new techniques and integrated instrumentation for multiscale monitoring high natural risk areas, such as: volcanic, seismic, energy exploitation, slope instability, floods, coastal instability, climate changes and other environmental context.
We expect contributions derived from several disciplines, such as applied geophysics, geology, seismology, geodesy, geochemistry, remote and proximal sensing, volcanology, geotechnical, soil science, marine geology, oceanography, climatology and meteorology. In this context, the contributions in analytical and numerical modeling of geological and environmental processes are also expected.
Finally, we stress that the inter-disciplinary studies that highlight the multiscale properties of natural processes analyzed and monitored by using several methodologies are welcome.

Smart monitoring and observation systems for natural hazards, including satellites, seismometers, global networks, unmanned vehicles (e.g., UAV), and other linked devices, have become increasingly abundant. With these data, we observe the restless nature of our Earth and work towards improving our understanding of natural hazard processes such as landslides, debris flows, earthquakes, floods, storms, and tsunamis. The abundance of diverse measurements that we have now accumulated presents an opportunity for earth scientists to employ statistically driven approaches that speed up data processing, improve model forecasts, and give insights into the underlying physical processes. Such big-data approaches are supported by the wider scientific, computational, and statistical research communities who are constantly developing data science and machine learning techniques and software. Hence, data science and machine learning methods are rapidly impacting the fields of natural hazards and seismology. In this session, we will see research from natural hazards and seismology for processes over a broad range of time and spatial scales.

Dr. Pui Anantrasirichai of the University of Bristol, UK will give the invited presentation:
Application of Deep Learning to Detect Ground Deformation in InSAR Data

Data science, analytics and visualization technologies and methods emerge as significant capabilities for extracting insight from the ever growing volume and complexity of scientific data. The rapid advancement of these capabilities no doubt helps address a number of challenges and present new opportunities in improving Earth and Space science data usability. This session will highlight and discuss the novelty and strength of these emerging fields and technologies of these components, and their trends. We invite papers and presentations to examine and share the experience of:
- What benefits they offer to Earth and Space Science
- What science research challenges they address
- How they help transform science data into information and knowledge
- In what ways they can advance scientific research
- What lessons were learned in the development and infusion of these methods and technologies

Most of the processes studied by geoscientists are characterized by variations in both space and time. These spatio-temporal phenomena have been traditionally investigated using linear statistical approaches, as in the case of physically-based models and geostatistical models. Additionally, the rising attention toward machine learning, as well as the rapid growth of computational resources, opens new horizons in understanding, modelling and forecasting complex spatio-temporal systems through the use of stochastics non-linear models.
This session aims at exploring the new challenges and opportunities opened by the spread of data-driven statistical learning approaches in Earth and Soil Sciences. We invite cutting-edge contributions related to methods of spatio-temporal geostatistics or data mining on topics that include, but are not limited to:
- advances in spatio-temporal modeling using geostatistics and machine learning;
- uncertainty quantification and representation;
- innovative techniques of knowledge extraction based on clustering, pattern recognition and, more generally, data mining.
The main applications will be closely related to the research in environmental sciences and quantitative geography. A non-complete list of possible applications includes:
- natural and anthropogenic hazards (e.g. floods; landslides; earthquakes; wildfires; soil, water, and air pollution);
- interaction between geosphere and anthroposphere (e.g. land degradation; urban sprawl);
- socio-economic sciences, characterized by the spatial and temporal dimension of the data (e.g. census data; transport; commuter traffic).