The session presents the state of art information systems in oceanography (metadata, vocabularies, ISO and OGC applications, data models), interoperability (Interoperability forms, Web services, Quality of Services, Open standards), data circulation and services (quality assurance / quality control, preservation, network services) and Education in ocean science (Education and Research, Internet tools for education).
The 2019 session should provide new ideas on the interoperability issues deriving from different sources of data and new data streams.
ISO standards introduce the necessary elements in the abstract process aiming to assess ‘how’ and ‘how much’ data meets applicable regulatory requirements and aims to enhance user needs. Data management infrastructures should include an evaluation of data by assuring relevance, reliability and fitness-for-purposes / fitness-for-use, adequacy, comparability and compatibility. The session aims also to create a link to the important initiatives on ocean literacy. Presenters are strongly encouraged to demonstrate how their efforts will benefit their user communities, facilitate collaborative knowledge building, decision making and knowledge management in general, intended as a range of strategies and practices to identify, create, represent and distribute data, products and information.

The quality of predictions of weather and climate depends on both resolution and complexity of the models that are used. However, resolution and complexity are limited by the computational performance that is available on today's supercomputers. While weather and climate models run on some of the fastest supercomputers of the world, models typically fail to run close to peak performance such that there is still room for a significant speed-up if efficiency is improved. The increase in parallelisation in high performance computing and the availability of various computing platforms is imposing significant challenges for the community to find the optimal hardware/model configuration and to achieve the best performance. On the other hand, the evaluation of high resolution simulations is often tedious due to large data volumes, limited statistic that is affordable and changed model behaviour that needs to be studied (e.g. if convection or eddies are resolved explicitly or if non-hydrostatic equations need to be used).
These challenges can only be addressed appropriately in a close collaboration between Computing and Earth System Scientists. This session aims to bring together scientists who run and evaluate atmosphere and ocean models with high resolution and complexity as well as scientists who enable these models to run as efficiently as possible on existing and future high performance computing architectures (regarding both model development and model optimisation). The session will also be an opportunity for scientists from the EU projects PRIMAVERA, ESCAPE and ESiWACE as well as HighResMIP from CMIP6 to meet and interact.

V. Balaji from Princeton University will be our keynote speaker invited by the ESiWACE EU Horizon2020 COE (grant number 675191).

A wide range of processes in the earth system directly affect geodetic observations. This session invites a wide array of contributions which showcase the use of geodesy for Earth science and climate applications, providing crucial insights into the state and change of the earth system and/or understanding its processes.

Data driven quantification of water mass fluxes through boundaries of Earth’s different regions and spheres provides important insights to other geoscience communities and informs model validation and improvement. Changes in regional sea level and ocean circulation are observed by altimetry and gravimetry. Natural and anthropogenic alterations of the terrestrial water cycle lead to changes in river runoff, precipitation, evapotranspiration, and water storage which may cause surface deformation sensed by GNSS stations and InSAR measurements as well as mass/gravity changes observed by satellite/ground gravimetry. Mass changes in the ice sheets and glaciers are detectable by both geometrical and gravimetric techniques. And other novel applications of geodetic techniques are emerging in many fields.

In addition, individual sensor recordings are often affected by high-frequency variability caused by, e.g., tides in the solid Earth, oceans, and atmosphere and their corresponding crustal deformations affecting station positions; non-tidal temperature and moisture variability in the troposphere modifying microwave signal dispersion; rapid changes in the terrestrially stored water caused by hydrometeorologic extreme events; as well as swift variations in relative sea-level that are driven by mass and energy exchange of the global oceans with other components of the Earth system, which all might lead to temporal aliasing in observational records. 

This session invites a wide array of contributions which showcase the use of geodesy for Earth science and climate applications. This session aims to cover innovative ways to use GRACE, GRACE-FO and other low Earth orbiters, GNSS techniques, InSAR, radar altimetry, and their combination with in-situ observations. We welcome approaches which tackle the problem of separating signals of different geophysical origin, by taking advantage of model output and/or advanced processing and estimation techniques. Since the use of geodetic techniques is not always straightforward, we encourage authors to think of creative ways to make their findings, data and software more readily accessible to other communities in hydrology, ocean, cryospheric, atmospheric and climate sciences. With author consent, highlights from the oral and poster session will be tweeted with a dedicated hashtag during the conference in order to increase the impact of the session.

The new scenario related to the global urbanization process and its impact on environmental sustainability and resilience to natural disasters, especially the ones related to the Climate Change, strongly call holistic multidisciplinary and multi-sectorial approaches for the management of urban areas and Cultural heritages.
These approach aim at providing solutions based on the integration of technologies, methodologies and best practices (remote and local monitoring, simulating and forecasting, characterizing, maintaining, restoring, etc.), with the purpose to increase the resilience of the assets, also thanks to the exploitation of dedicated ICT architectures and innovative eco-solutions and also by accounting the social and economic value of the investigated areas, especially in CH frame.
In this context, attention is also focused on the high-resolution geophysical imaging is assuming a great relevance to manage the underground and to adopt new strategies for the mitigation of geological risks.
This session represents a good forum to present, technologies best practices and share different experiences in the field of the urban areas and CH management and protection, against the multi-risk scenarios and for the different situations at European and worldwide level. Finally, great attention will be devoted to the success cases, with a specific focus on recent international projects on smart cities and Cultural heritage in Europe and other countries.

From the perspective of Earth System predictions, the use of machine learning, and in particular deep learning, is still in its infancy. There are many possible ways how machine learning could improve model quality, generate significant speed-ups for simulations or help to extract information from numerous Earth System data, in particular satellite observations. However, it has yet to be shown that machine learning can hold what it is promising for the specific needs of the application of Earth System predictions. This session aims to provide an overview how machine learning can/will be used in the future and tries to summarise the state-of-the-art in an area of research that is developing at a breathtaking pace.

Remarkable technological progress in remote sensing and geophysical surveying, together with the recent development of innovative data treatment techniques are providing new scientific opportunities to investigate landslide processes and hazards all over the world. Remote sensing and geophysics, as complementary techniques for the characterization and monitoring of landslides, offer the possibility to effectively infer and correlate an improved information of the shallow -or even deep- geological layers for the development of conceptual and numerical models of slope instabilities. Their ability to provide integrated information about geometry, rheological properties, water content, rate of deformation and time-varying changes of these parameters is ultimately controlling our capability to detect, model and predict landslide processes at different scales (from site specific to regional studies) and over multiple dimensions (2D, 3D and 4D).

This session welcomes innovative contributions and lessons learned from significant case studies using a myriad of remote sensing and geophysical techniques and algorithms, including optical and radar sensors, new satellite constellations (including the emergence of the Sentinel-1A and 1B), Remotely Piloted Aircraft Systems (RPAS) / Unmanned Aerial Vehicles (UAVs) / drones, high spatial resolution airborne LiDAR missions, terrestrial LIDAR, Structure-from-Motion (SfM) photogrammetry, time-lapse cameras, multi-temporal Synthetic Aperture Radar differential interferometry (DInSAR), GPS surveying, Seismic Reflection, Surface Waves Analysis, Geophysical Tomography (seismic and electrical), Seismic Ambient Vibrations, Acoustic Emissions, Electro-Magnetic surveys, low-cost (/cost-efficient) sensors, commercial use of small satellites, Multi-Spectral images, Real time monitoring, in-situ sensing, etc.

The session will provide an overview of the progress and new scientific approaches of Earth Observation (EO) applications, as well as of surface- and borehole-based geophysical surveying for investigating landslides. A special emphasis is expected not only on the collection but also on the interpretation and use of high spatiotemporal resolution data to characterize the main components of slope stability and dynamics, including the type of material, geometrical and mechanical properties, depth of water table, saturation conditions and ground deformation over time. The discussion of recent experiences and the use of advanced processing methods and innovative algorithms that integrate data from remote sensing and geophysics with other survey types are highly encouraged, especially with regard to their use on (rapid) mapping, characterizing, monitoring and modelling of landslide behaviour, as well as their integration on real-time Early Warning Systems and other prevention and protection initiatives. Other pioneering applications using big data treatment techniques, data-driven approaches and/or open code initiatives for investigating mass movements using the above described techniques will also be considered on this session.

We invited prof. Denis Jongmans (Isterre, Université Grenoble Alpes, France), as guest speaker for the session.

From the real-time integration of multi-parametric observations is expected the major contribution to the development of operational t-DASH systems suitable for supporting decision makers with continuously updated seismic hazard scenarios. A very preliminary step in this direction is the identification of those parameters (seismological, chemical, physical, biological, etc.) whose space-time dynamics and/or anomalous variability can be, to some extent, associated with the complex process of preparation of major earthquakes.
This session wants then to encourage studies devoted to demonstrate the added value of the introduction of specific, observations and/or data analysis methods within the t-DASH and StEF perspectives. Therefore studies based on long-term data analyses, including different conditions of seismic activity, are particularly encouraged. Similarly welcome will be the presentation of infrastructures devoted to maintain and further develop our present observational capabilities of earthquake related phenomena also contributing in this way to build a global multi-parametric Earthquakes Observing System (EQuOS) to complement the existing GEOSS initiative.
To this aim this session is not addressed just to seismology and natural hazards scientists but also to geologist, atmospheric sciences and electromagnetism researchers, whose collaboration is particular important for fully understand mechanisms of earthquake preparation and their possible relation with other measurable quantities. For this reason all contributions devoted to the description of genetic models of earthquake’s precursory phenomena are equally welcome. Every 2 years selected papers presented in thsi session will be proposed for publication in a dedicated Special Issue of an international (ISI) scientific journal.

In recent years an increasing number of research projects focused on natural hazards (NH) and climate change impacts, providing a variety of information to end user or to scientists working on related topics.

The session aims at promoting new and innovative studies, experiences and models to improve risk management and communication about natural hazards to different end users.

End users such as decision and policy makers or the general public, need information to be easy and quickly interpretable, properly contextualized, and therefore specifically tailored to their needs. On the other hand, scientists coming from different disciplines related to natural hazards and climate change (e.g., economists, sociologists), need more complete dataset to be integrated in their analysis. By facilitating data access and evaluation, as well as promoting open access to create a level playing field for non-funded scientists, data can be more readily used for scientific discovery and societal benefits. However, the new scientific advancements are not only represented by big/comprehensive dataset, geo-information and earth-observation architectures and services or new IT communication technologies (location-based tools, games, virtual and augmented reality technologies, and so on), but also by methods in order to communicate risk uncertainty as well as associated spatio-temporal dynamic and involve stakeholders in risk management processes.

However, data and approaches are often fragmented across literature and among geospatial/natural hazard communities, with an evident lack of coherence. Furthermore, there is not a unique approach of communicating information to the different audiences. Rather, several interdisciplinary techniques and efforts can be applied in order to simplify access, evaluation, and exploration to data.

This session encourages critical reflection on natural risk mitigation and communication practices and provides an opportunity for geoscience communicators to share best methods and tools in this field. Contributions – especially from Early Career Scientists – are solicited that address these issues, and which have a clear objective and research methodology. Case studies, and other experiences are also welcome as long as they are rigorously presented and evaluated.

New and innovative abstract contributions are particularly welcomed and their authors will be invited to submit the full paper on a special issue on an related-topics Journal.

In cooperation with NhET (Natural hazard Early career scientists Team).

The session aims to collect original or review contributions on the use of data from Low-Earth-Orbiting (LEO) satellites making measurements in the thermosphere-ionosphere to investigate ionospheric anomalies related to space weather, geophysical and artificial sources. In fact, data from LEO satellites can provide a global view of near-Earth space variability and are complementary to ground-based observations, which have limited global coverage. The AMPERE project and integration of the Swarm data into ESA’s Space Weather program are current examples of this. The availability of thermosphere and ionosphere data from the DEMETER satellite and the new operative CSES mission demonstrates that also satellites that have not been specifically designed for space weather studies can provide important contributions to this field. On the other hand, there are evidences that earthquakes can generate electromagnetic anomalies into the near Earth space. A multi-instrumental approach, by using ground observations (magnetometers, magnetotelluric stations, GNSS receivers, etc.) and LEO satellites (DEMETER, Swarm, CSES, etc.) measurements can help in clarifying the missing scientific knowledge of the lithosphere-atmosphere-ionosphere coupling (LAIC) mechanisms before, during and after large earthquakes. We also solicit contributions on studies about other phenomena, such as tropospheric and anthropogenic electromagnetic emissions, that influence the near-Earth electromagnetic and plasma environment on all relevant topics including data processing, data-assimilation in models, space weather case studies, superimposed epoch analyses, etc.

With the impressive theoretical progress of last decades, the global tectonics is about to reach a state that is quite unique not only for geology but for any descriptive domain of knowledge. This is the state of so high elaboration and maturity that a theory may be subject, like some theories of physics, chemistry, algebra and geometry, to the most rigorous inspection ever suggested in the science – the inspection for being formal sensu mathematical logic. Still, to bring the global tectonics to this state, quite a work remains to be done. This is an exciting cross-disciplinary work of knowledge engineers and geologists that would result in a quite new level of understanding the Earth and new quality of scientific collaboration on it.
However, being so different from all the fields that underwent such “high formalization” so far, the tectonics needs special formal treatment, which, in turn, requires special logico-mathematical formalism complementary to the traditional predicate logic. Thus the scope of this session appears highly cross-disciplinary, claiming for a joint intellectual journey of field geologists, experimentalists and modelers, IT specialists and computer scientists, logicians and mathematicians.

Increase in the amount of high quality seismic data and advances in high-performance computing in recent years have been transformative to explore Earth’s interior at all scales through seismic modelling, both in theory and practice. The goal of this session is to bring seismologists and computational scientists together to discuss recent advances and future directions in innovative forward & inverse modelling techniques, HPC systems & computational tools as well as the related theory and scientific outcomes.

We encourage contributions in the field of theoretical and computational seismology highlighting, but not limited to;

- advancements in numerical solvers and techniques,
- seismic codes on emerging CPU/GPU architectures
- full-waveform inversion from local to global scales,
- Bayesian inverse problems,
- machine learning algorithms for seismic problems,
- big data (seismic & computational) problems,
- large-scale workflows on HPC systems and their automatization,
- optimization strategies,
- uncertainty analysis for large-scale imaging,
- seismological results of HPC applications from passive (earthquakes and noise) and active seismic sources,
- visualization (parallel, VR platforms, etc. ).

Ensuring long-term water sustainability for increasing human populations is a common goal for water resource managers. Measuring evapotranspiration (ET) at watershed or river-reach scales, upland or urban areas is required to estimate how much water can be apportioned for human needs while maintaining healthy vegetation and habitat for wildlife.
Consequently, much research has been devoted to this topic. However although there have been many advances in meteorological equipment and observations, more universal recognition of the impact of climate and land cover changes on evaporation and hydrology, and the increased accessibility of many parts of the world, evaporation from much of the globe remains elusive to quantify. This is particularly true in areas with few meteorological observations, in regions where precipitation is particularly hard to predict such as in arid and semi-arid or mountain environments. ET measurements are often made on local scales, but scaling up has been problematic due to spatial and temporal variability.
There are challenges associated with handling temporal variability over complex agro-climatic regions and in places with strong effects of unpredictable climate oscillations. For instance, crop/plant coefficients vary seasonally, particularly for riparian, upland vegetation, and urban greenery; traditional approaches of ET estimation commonly neglect the heterogeneity of microclimate, density, species, and phenology that have often led to gross overestimates of plant water use.
In this session, we want to focus on quantifying evapotranspiration dynamics in diverse climates and environments as a tool for improving hydrologic assessments and predictions at a catchment scale. Remote sensing products in many cases are the only spatially distributed information available to account for seasonal climate and vegetation variability and are thus extremely valuable data sources for ET estimation on larger scales.
We invite researchers to contribute theoretical and empirical ET model applications for a variety of dryland vegetation associations and other sensitive environments. We welcome studies that estimate ET using both prognostic and diagnostic approaches from process-based models that rely on the integration of precipitation and soil-vegetation dynamics to a more direct estimation of ET using e.g. remote sensing based data streams. Applications in drought-prone forests, rangelands, mountain and urban areas at a range of spatial and temporal scales are encouraged.

Global losses due to natural hazards have shown an increasing trend over the last decades, which is expected to continue due to growing exposure in disaster-prone areas and the effects of climate change. In response, recent years have seen greater worldwide commitment to reducing disaster risk. Working towards this end requires the implementation of increasingly effective disaster risk management (DRM) strategies. These must necessarily be supported by reliable estimates of risk and loss before, during, and after a disaster. In this context, innovation plays a key role.
This session aims to provide a forum to the scientific, public and private discourse on the challenges to innovate DRM. We welcome submissions on the development and application of groundbreaking technologies, big data, and innovative modeling and visualization approaches for disaster risk assessment and DRM decision-making. This includes the quantification and mapping of natural hazard risks and their components (i.e. hazard, exposure, and vulnerability), as well as the forecasting of hazard and impacts prior to a disaster event, or as it is unfolding (in real- or near real-time). We are particularly interested in contributions covering one or more of the following thematic areas in the context of disaster risk assessment and reduction: artificial intelligence and machine learning, big data, remote sensing, social media, volunteered geographic information (VGI), mobile applications, crowdsourcing, internet of things (IoT), and blockchain. We also welcome submissions exploring how these or other innovations can support real-world DRM strategies and translate into improved DRM decisions.

The application of Earth Observation (EO) datasets for Sustainable Development is a fast-growing field. EO technologies and innovations are constantly evolving, and contributing to the delivery of sustainable, economic and societal benefit to developing countries, helping them meet their Sustainable Development Goals. There is great potential to build on the unique strengths that the space sector has in terms of services and technology to deliver sustainable development objectives, especially in data-sparse regions of the globe, and realising this potential is crucial. The scientific and socio-economic benefits from remote sensing data applications are limitless. Especially in developing nations, where there is a need to bridge the gap between existing technologies and operational applications, EO technology can help enhance the capability to monitor the Earth’s vital resources, and to support the planning, design, operation, and management processes of various sectors.

This session invites submissions from researchers and practitioners, whose work with EO technologies provides the information needed to confront key sustainable development challenges, spanning a whole range of themes such as: disaster response and early warning systems, water resources, agriculture, air and water quality, deforestation, land-use change, urban development, renewable energy and health.

Satellite data provides information on the marine environment that can be used for many applications – from water quality and early warning systems, to climate change studies and marine spatial planning. The most modern generation of satellites offer improvements in spatial and temporal resolution as well as a constantly evolving suite of products.

Data from the European Union Copernicus programme is open and free for everyone to use however they wish - whether from academic, governance, or commercial backgrounds. The programme has an operational focus, with satellite constellations offering continuity of service for the foreseeable future. There is also a growing availability of open source tools that can be used to work with this data.

This short course is an opportunity to learn about the data available from the Copernicus Sentinel 3 satellite, and then, with support from marine Earth Observation experts, to develop your own workflows for using data from the EUMETSAT Copernicus Marine Data Stream and Copernicus Marine Environment Monitoring Service. The sessions will be interactive, using the WeKEO DIAS hosted processing, Sentinel Applications Platform (SNAP) software, and Python programming. No experience is necessary as various exercises will be provided for a wide range of skill levels and applications, however participants should bring their own laptops and be prepared to install open source software in advance.

The ENES Climate Analytics Service (ECAS) is a new service from the EOSCHUB project. It enables scientific end-users to perform data analysis experiments on large volumes of climate data, by exploiting a PID-enabled, server-side, and parallel approach.
It aims at providing a paradigm shift for the ENES community with a strong focus on data intensive analysis, provenance management, and server-side approaches as opposed to the current ones mostly client-based, sequential and with limited/missing end-to-end analytics workflow/provenance capabilities.

This short course is divided into a teaching as well as a hands on training part and includes:
- presentation(s) on the theoretical and technical background of ECAS. This covers the data cube concept and its operations (eg.: subset extraction, reduction, aggregation). Furthermore, we provide an introduction to the Ophidia framework, which is the components of ECAS for processing multidimensional data.
- tutorials and training materials. Participants will have the opportunity to dive into the ECAS software stack and learn how to manipulate multidimensional data through real world use cases from the climate domain.

This short course is open to everyone interested in processing multidimensional data. ECAS is server-based, thus all required software and tools are already available on our sites. Participants do not need to install any software stack on their laptop. All they need is a browser to access the ECAS portal. Only a prior registration is required and it is straightforward by following these links: https://ecaslab.dkrz.de/registerproc.html or https://ophidialab.cmcc.it/web/registration.html

During this short course, the participants will learn:
- what the data cube concept is and how is manipulated with ECAS/Ophidia
- how to perform analysis on multidimensional data
- how to publish, access and share data and workflows with ECAS
- how to implement/deploy their own scientific workflows

Public information:
When: 10 April 2019
Where: Room -2.31

R is probably the most important statistical computing language in academia. With more than 10,000 packages it has been extended in many directions, including a huge support for geospatial data (see https://cran.r-project.org/web/views/Spatial.html and Bivand, Pebesma, and Gómez-Rubio 2013). R’s flexibility and statistical capabilities have made it attractive for people working in Earth, planetary and space sciences and a need for geographic data science.

This course will introduce the audience to R’s geographical capabilities, building on the book Geocomputation with R (https://geocompr.robinlovelace.net/) by the workshop authors (Lovelace, Nowosad, and Muenchow 2018). It will cover four topics and provide a solid foundation for attendees to apply R to a range of geographic data:

1. R’s implementation of the two most important spatial data models - vector (Pebesma 2018) and raster (Hijmans 2017).
2. Spatial data visualization with R.
3. Bridges to dedicated GIS software such as QGIS.
4. Statistical learning with geographic data.

Understanding data models is vital for working with geographic data in R. Maps, based on the data, can display complex information in a beautiful way while allowing for first inferences about spatial relationships and patterns. R has already become a Geographic Information System (GIS) (Bivand, Pebesma, and Gómez-Rubio 2013) - a system for the analysis, manipulation and visualization of geographic data (Longley et al. 2015). However, R was not designed as a GIS, and therefore computing large amounts of geographic data in R can be cumbersome. Even more important, R is missing hundreds of geoalgorithms which are readily available in common Desktop GIS. To deal with these shortcomings R packages have been developed allowing R to interface with GIS software. As an example, we will introduce the RQGIS package (Muenchow, Schratz, and Brenning 2017) for this purpose but also comment on other R-GIS bridges such as RSAGA (Brenning, Bangs, and Becker 2018) and rgrass7 (Bivand 2017). We will use RQGIS to compute terrain attributes (catchment area, catchment slope, SAGA wetness index, etc.) which we will subsequently use to model and predict spatially landslide susceptibility with the help of statistical learning techniques such as GLMs, GAMs and random forests (James et al. 2013). Hence, we show by example how to combine the best of two worlds: the geoprocessing power of a GIS and the (geo-)statistical data science power of R. The short course will consist of a mixture of presentations, live code demos and short interactive exercises if time allows.

Learning objectives
By the end of this workshop, the participants should:

- Know how to handle the two spatial data models (vector and raster) in R.
- Import/export different geographic data formats.
- Know the importance of coordinate reference systems.
- Be able to visualize geographic data in a compelling fashion.
- Know about geospatial software interfaces and how they are integrated with R (GEOS, GDAL, QGIS, GRASS, SAGA).
- Know about the specific challenges when modeling geographic data.

Software requirements
1. Latest version of R and RStudio
2. R packages: sf, raster, RQGIS, RSAGA, spData, tmap, tidyverse, mlr
3. QGIS (including SAGA and GRASS), please follow our installation guide (http://jannes-m.github.io/RQGIS/articles/install_guide.html) to make sure that RQGIS can work with QGIS

References
Bivand, Roger. 2017. Rgrass7: Interface Between GRASS 7 Geographical Information System and R. https://CRAN.R-project.org/package=rgrass7.

Bivand, Roger S., Edzer Pebesma, and Virgilio Gómez-Rubio. 2013. Applied Spatial Data Analysis with R. 2nd ed. New York: Springer.

Brenning, Alexander, Donovan Bangs, and Marc Becker. 2018. RSAGA: SAGA Geoprocessing and Terrain Analysis. https://CRAN.R-project.org/package=RSAGA.

Hijmans, Robert J. 2017. Raster: Geographic Data Analysis and Modeling. https://CRAN.R-project.org/package=raster.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani, eds. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics 103. New York: Springer.

Longley, Paul, Michael Goodchild, David Maguire, and David Rhind. 2015. Geographic Information Science & Systems. Fourth edition. Hoboken, NJ: Wiley.

Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2018. Geocomputation with R. The R Series. CRC Press.

Muenchow, Jannes, Patrick Schratz, and Alexander Brenning. 2017. “RQGIS: Integrating R with QGIS for Statistical Geocomputing.” The R Journal 9 (2): 409–28.

Pebesma, Edzer. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal. https://journal.r-project.org/archive/2018/RJ-2018-009/index.html.

Nowadays, researchers have to tailor their models, data and results into systems which can be used by non-experts, such as policy makers, stakeholders, farmers and the many professionals in need of clear answers to land management questions.

One way ahead to bridge the gap between R&D and real-life applications is the development of decision support systems (DSS) on top of geospatial cyberinfrastructures (GCI) that can handle end-user requests in real time with all the complexity being transparent to the user.

The short course will cover some developments carried out within the EU H2020 LandSupport Project. The implementation of an indicator of land-take is showed, both presenting how to deal with the technical steps on a more general level and proposing hands-on sessions on the implementation of specific components of the whole land-take workflow.

First an introduction is presented, covering a general overview about the GCI and the requirements of pipelines.
A brief description of the main tasks follows:

• Big spatio-temporal raster data are managed by means of rasdaman. Here a workflow is presented showcasing how to import and query multi-band Sentinel-2 data based on the OGC Big Data Standards.
• Cloud masking and filtering. Copernicus Sentinel-2 data are processed to obtain bottom of the atmosphere, cloud free, reflectance data. A theoretical and a hands-on session in R will be presented.
• Classification. A spectral-temporal datacube of Sentinel-2 data are used to get a binary map of imperviousness (1: urban pixel, 0: non-urban pixel). At least one classification model will be presented with hands-on in R and/or MatLab.
• Land-take. An algorithm to calculate land-take using a low-level programming language is showed, with more advanced insights about the opportunity to face GPU calculations.

Altogether, we motivate how the LandSupport approach aims at providing decision support based on multi-source spatiotemporal data in a user-centric manner.
Ample time will be available for answering questions and discussion.