Catálogo de publicaciones - libros

Traditional land use and land cover (LULC) mapping has long relied strongly on input from Earth Observation (EO) data sources at various resolutions and scale levels. With high performance and cloud computing on the rise, rapid processing of large volumes of very high resolution (VHR) satellite imagery—big EO data—is becoming less problematic. Consequently, scientific challenges in that topical domain move on to the next level.

Over the recent years, the “sharing economy”, which ranges from Airbnb to ZipCars, has emerged as a widely applicable business concept, with a substantial market potential: Various studies predict an annual volume of over 20 billion US-Dollars for the sharing economy (Foye 2016). The main value proposition lies in making otherwise unused resources (for example an unoccupied apartment, or a parked car) easily available to those who need them—temporarily, and on a pay-per-use basis. Companies like CloudEO aim to adapt this model to the market for Earth Observation services, which have lagged far behind in their potential—in the depth of geospatial information services adding value to the business workflows of the industry, as well as with respect to their overall market volume and potential.

EO based services and related products have been proven to be a stable source of information with reliable quality under varying soil and weather conditions. Information products, like the TalkingFields maps, allow the farmer to more accurately react with site-specific farming techniques. More accuracy means lower production costs, as resources such as water, seeds and fertilizer are not wasted. More accurate also means more efficient in the sense of yield per fertilizer or water used. Last but not least, more accurate also means more sustainable because less fertilizer is leached to the ground water. This benefits on the one side farmers with increased economic profits. On the other side, it also serves natural resource providers such as drinking water suppliers or environmental protection agencies, resulting in both successful commercial business and as well as environmental gains. All in all, smart farming supports ecologically and economically sound agricultural management via site-specific applications – an important step towards sustainable agriculture. Sentinel-2 already marks a huge step towards better spectral coverage and operational availability of satellite data. The free and open access to Sentinel-2 data and infrastructure platforms like FS-TEP will guarantee that these data can be used by everybody around the Globe, no matter whether they are from an industrialized country or a developing country. This gives equal chances to everybody to be more sustainable in agriculture.

Enterprise Resource Planning (ERP) is referring to a complex software system which helps to optimize business decisions for companies. The foundation for good business decisions is the right information and this raises the questions where to find this valuable information and how to integrate it into business processes. Companies are collecting and processing all kinds of data to find the right information to improve and make an effective business decision.

Multi-decadal data sets are already available from various types of EO sensors, but their effective exploitation is hindered by the lack of data centres which offer dedicated EO processing chains and high-performance processing (HPC) capabilities. Recognizing this need, TU Wien founded the EODC Earth Observation Data Centre for Water Resources Monitoring which aims at providing an independent science-driven platform that is transparent for its users and offers a high diversity and flexibility in terms of data sets and algorithms used.

As part of the global data revolution, an increasing number of World Bank projects are based on insights from big data sources, including satellite-based measurements. Many use innovative machine- and deep-learning techniques to understand factors key to development. Leading satellite-based initiatives include:

Putting big data innovation into everyday practice requires collaboration between data scientists, technologists and sector specialists. The World Bank’s experience has shown the value of learning by doing, collaboration and persistence.

While environmental and social threats to society changes faster than in recent centuries, there is more of a need for faster, globally scalable and locally relevant risk information from developing Banks and the countries they serve. Big Data can range from gigabytes (call details records), to terabytes (satellite data), to petabytes (web traffic), with each magnitude requiring unique algorithms to extract the signal from the noise. This chapter explores how one type of sensor data—satellite imagery—can be made more useful through the development of an application that leverages Cloud Computing—Google Earth Engine—to turn data into insight for decision-makers on the ground.

With an ever-increasing wealth of earth science data produced from various sources and platforms including earth observation, modeling and forecasts, the opportunities to exploit such vast amounts of data to produce valuable information products are challenging and exciting. These data are widely used for monitoring, simulation and analysis of measurements that are associated with physical, chemical and biological phenomena across the ocean, atmosphere and land. The volume, variety, and complexity of multidimensional earth science data have posed challenges in the past with how it is shared with a diverse community, visualized intuitively, or integrated for answering scientific questions. In this chapter, we will share broadly how geospatial technology, and Esri technology, is evolving to better support earth observation scientific data management, analysis and visualization. Methods for sharing data through information products as web services and tailored applications that expand discovery and access to a larger user community using a suite of ArcGIS web apps will be discussed. We will then briefly explore the suite of ArcGIS web applications and how they are revolutionizing the way we communicate and tell stories with dynamic data-driven apps such as Esri Story Map. Lastly, a brief overview of how Esri is moving towards improving BigData capabilities will be provided. These methods and applications, publishing and consuming services and performing analyses are key drivers to advancing innovation.

Earth-observation systems (satellites and in situ monitoring) are routinely used to collect information about water quality. Recently, smartphone-based tools and other citizen-science sensors have enabled citizens to also contribute to the collection of scientifically relevant data. This chapter describes a decision support system used to predict optical water-quality indicators in the Wadden Sea, which is an intertidal marine system, where natural processes related to sediment transport and primary production define the basis of its ecological values. As information sources, the system uses satellite data, data collected with a mobile app and physical data for the period 2003–2015. An artificial-intelligence technique, inductive learning, is used to analyze the data and provide predictions in terms of water colour represented via the Forel-Ule scale (a comparative scale for colour).