EO21: Earth Observation Market in the 21st Century. Indicator of Trends Report by Francesco Liucci

TABLE OF CONTENTS EXECUTIVE SUMMARY.................................. 3 BACKGROUND AND CONTEXT

DATA ECONOMY............................................ 5 TECHNOLOGICAL Data volume increasing Processing costs declining Sensor use growing Creating value User interfaces improving HUMAN Disruptive innovation Innovation examples Business models CULTURAL Free data demand Open data policies App development Data privacy

5 6 9 11 12 14 14 15 17 17 18 18

EO MARKET...................................................... 20 TECHNOLOGICAL Volume of data increasing Satellite capabilities improving Data sources diversifying HUMAN New business models Thematic Platforms EO value chain CULTURAL Access to data Data price reduction

20 22 23 25 25 26 27 28

CONCLUSIONS.............................................. 30

Indicator of Trends Report

EXECUTIVE SUMMARY Rapid advances in technologies, such as cloud computing, the Internet of Things (IoT) and autonomous systems, are creating an ever increasing amount of data available to governmental agencies, companies and citizens. The volume of data is rising rapidly and ‘Big Data’ acts as the oil in the supply chain for many industries. In the next five years, ESA spacecraft alone will obtain approximately 25 petabytes of Earth Observation (EO) data as a result of the Copernicus programme1. In addition, data is generated from a multitude of sources, including satellites, ground and airborne sensors (e.g. Unmanned Aerial Vehicles, UAVs), social media, machine to machine (M2M) communications and crowdsourcing. The cost to process and store data is falling, making it simpler and more economical to capture larger datasets by leveraging the significant investment made by companies in the cloud computing industry. Increasing value lies in

turning this data into knowledge and actionable insights, thereby enabling new applications and services that create value for end users. With views into daily activity being refreshed at a faster rate than ever before, just selling raw pixels is not enough to satisfy end user demands, those pixels need to be turned into insights. This is evident in the EO sector where ambitious start-ups, such as Planet Labs, are building constellations of small satellites and developing cutting-edge analytics to extract value from the data captured. Many of these start-ups consider themselves as satellite powered data companies. In Planet Labs’ most recent round of funding (€80m), the company plans to use the investment to develop its capabilities for processing, interpreting and selling data contained in its images. It was this focus that attracted interest from Data Collective, a venture capital firm, which has backed several big data start-ups2.

Data Value Chain

Source: Digital Catapult (2014)3

More EO missions are being launched than ever before. Reduced launch costs, miniaturisation of technology, improved on-board processing and better reliability are driving increased interest in small satellites by new commercial companies. To unlock the economic potential of data from the increasing number of satellites, public agencies and private companies are creating data products that aim to be responsive to user needs. The satellite data generated from ESA’s Sentinel satellite constellation, for example, will provide actionable insights from the observation of the planet thanks to an array of sensor technologies, including Synthetic Aperture Radar (SAR) and Multispectral/Hyperspectral sensors.

EO as a Platform turns raw data into knowledge through processing and analysis, creating value within and across various sectors. EO and remote sensing data has significant potential to help us manage the modern world and our planet’s resources. Applications and services are already emerging for emergency response and environmental monitoring, while emerging markets such as precision agriculture, monitoring of illegal fishing and management of natural resources are rapidly developing. There is increasing value to be created by reaching more customers through the applications of big data. The EO data value chain creates opportunity for small and medium sized enterprises (SMEs) and start-ups to engage with the

Di Meglio et al (2014) CERN Openlab Whitepaper on Future IT Challenges in Scientific Research, see www.zenodo.org/record/8765# retrieved on 2nd February 2015 Financial Times (2015), see www.ft.com/cms/s/0/7805f624-a08b-11e4-8ad8-00144feab7de.html?siteedition=uk#axzz3PRjcpl00 retrieved on 21st January 2015 3 Digital Catapult (2014), Data Value Chain, see www.digital.catapult.org.uk/data-value-chain retrieved on 22nd December 2014 1 2

Executive Summary

space sector, and generate value from satellite missions by developing applications for citizens, local government and commercial industry. Public agencies are increasingly interested in how they can interact effectively with companies that have enabled a globally distributed applications ecosystem and are investing extensively in cloud computing infrastructure. Content Delivery Networks (CDNs), like Microsoft Azure and Amazon’s CloudFront, are key enablers of building, deploying and managing scalable applications

with the aim of reaching a global audience. Open data policies can enable the private sector to do just that, and reach a wide audience of application developers and end users. According to The Economist, information held by governments in Europe could be used to generate an estimated €140 billion worth of value a year4. In short, making official data public will spur innovation and create new applications. These benefits need to be balanced against the rights of individuals to have their data protected by government.

Summary - Value Chain, Key Drivers of Change

BACKGROUND AND CONTEXT Acquiring a better understanding of the changing landscape of Earth Observation technologies and markets in the 21st Century, by identifying the key drivers of change in the digital economy and EO sector (including technological, human, cultural and legal factors), is essential to providing context on which to build a strategy. This “Indicator of Trends” report will aim to address the key areas of change from technological developments to new business models. The overall objective of the EO21 study is as follows: “Provide a unifying context and strategy to identify, analyse and capitalise on the key technological opportunities affecting the EO sector in the emerging data economy, from the collection of EO data to their exploitation in science and business5.” The emergence of cloud computing is already transforming the way we access and exploit data.Today, applications that were once performed by EO experts are now pushed to a cloud-based environment, such as the Google Earth Engine

or Amazon S3, and are processed quickly and cheaply by a wide range of users (including non-space users).This has led to a paradigm shift in the method of distributing and processing big data, and in creating platforms that drive innovation and growth in user applications. It is expected that the influence of these drivers of change will have profound implications on how we interact with, access and store EO data. This will lead to an explosion of applications in this area, which are likely to be one of the main “growth engines of the space economy” according to the UK’s Space Innovation & Growth Strategy report. Assessing these global trends and mechanisms, and how they impact the EO sector, is an important step that will inform the rest of the EO21 study. This report delivers findings and insights from a comprehensive review of publicly available sources and forms part of an overall study commissioned by the European Space Agency (ESA), in partnership with the Satellite Applications Catapult, on the changing landscape of Earth Observation technologies and markets in the 21st century (EO21)

The Economist (2013) A new goldmine; Open data, see www.economist.com/news/business/21578084-making-official-data-public-could-spur-lots-innovation-new-goldmine retrieved on 8th February 2015 5 ESA (2014), EO21 Statement of Work 4

Indicator of Trends Report

DATA ECONOMY Technological DATA VOLUME INCREASING RAPIDLY • Big data - the volume, variety, velocity and value of data is increasing • Information layers - acquiring data is no longer about one golden source of information but rather about a variety of sources. • Linked data/Semantic Web - through connected sources, data presents itself to the user more readily and is more useful and relevant. 1) Big Data Large bodies of information can now be examined, processed and utilised for a multitude of purposes. Mobile technology and cloud computing use data collection to find new ways of capturing value and increasing the power of data across the entire spectrum of sectors. Like other essential factors of production such as physical assets and human capital, it is increasingly the case that much of modern economic activity, innovation, and growth simply couldn’t take place without data. The key characteristics of the big data trend are6: • Volume – the size of data is increasing fast. One Exabyte of data travels over the internet every day. IP traffic per capita will grow from 4 gigabytes in 2011 to 15 gigabytes in 2016. 43 trillion gigabytes of data will be created by 2020, an increase of 300 times from 2005.7 • Variety – data diversity is growing. Sensor data and smartphone logs are adding variety. There are over 1 billion Facebook users, 2/3 of whom log in monthly through a mobile device. 82% of 18 to 29 year olds utilise a form of social networking. • Velocity – data is arriving at a faster rate. Streaming videos increase velocity. The sum of all forms of video will continue to be approximately 86% of global consumer traffic by 2016. • Value – data mining and analytic capability is improving. Turning raw data into knowledge through processing and analysis can lead to disruptive outputs and the creation of value for the end user. • Veracity – There is a key role for providers of data to

build trusted datasets as uncertainty of data persists. According to IBM, 1/3 of business leaders don’t trust the information they use to make decisions and poor data quality costs the US economy around $3.1 trillion a year8. 2) Information Layers The increased volume, variety and velocity of data is generated from a multitude of data sources. Data processors are becoming more source agnostic than ever before and are exposed to ever larger data sets. Combining multiple sources of information, for example satellites and Unmanned Aerial Vehicles (UAVs) in EO applications, can generate complementary data enriched through processing and analysis. To illustrate this point, the Facebook-backed not-for-profit, internet.org, aims to change the way in which the internet is distributed, so that 2/3 of the world’s population that don’t currently have internet access can reap the benefits that the internet brings. Through its Connectivity Lab, Facebook is investigating using a combination of satellites, solar powered high altitude UAVs, and lasers to tie these solutions together and communicate between platforms. Citizens are also sources of information and analysis. With the internet and power of computing, crowdsourcing - the process of connecting with large groups of people for their knowledge, expertise, time or resources - is now a way of solving problems quickly through multiple participants. Crowdsourcing combines the efforts of numerous volunteers, where each contributor adds a small portion to the overall effort. For example during the search for flight MH370 in 2014, the early stage search was not just limited to aircraft and ships. A digital crowdsearching effort ran alongside this, mobilising as many as 8 million digital volunteers in total9. DigitalGlobe posted the following report at the time of the search: “Our constellation of five high-resolution imaging satellites captures more than 3 million square kilometres of earth imagery each day, and this volume of imagery is far too vast to search through in real time without an idea of where to look. We have been applying our satellite resources over a broader area than the official search area, while only focusing the efforts of our Tomnod crowdsourcing volunteers on the search areas identified by authorities. The efforts of millions of online volunteers around the world allowed us to rule out broad swaths of ocean with some certainty10.” - The Straits Times, 2014

Credit Suisse (2013), Big Data:Taking a Quantum Leap IBM website (2015), Big Data and Analytics Hub, see www.ibmbigdatahub.com/infographic/four-vs-big-data accessed 15th March 2015 9 ESA (2014) Conference on Big Data from Space, Citizen Cyberscience Session. ESRIN, Italy 10 The Straits Times (2014), see www.straitstimes.com/breaking-news/se-asia/story/worldview-2-the-satellite-could-have-found-malaysia-airlines-flight-mh37 retrieved on 8th February 2015 6

7-8

Data Economy

Crowdsourcing creates different perspectives, providing varying interpretations of data and enabling an understanding of the realistic range of possibilities. 3) Linked Data/Semantic Web The semantic web aims to convert the current web, dominated by unstructured and semi-structured documents, into linked data. The semantic web is a vision of information that can be readily interpreted by machines, so that they can perform more of the tedious work involved in finding, combining, and acting upon information on the web. Machine learning is a scientific discipline that deals with the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions. Machine learning has strong ties to artificial intelligence - the development of computers with skills that traditionally would have required human intelligence, such as decision-making and visual perception. Although at an early stage, the semantic web is already a reality. Google is building a large database of entities people, places and things.This database is highly structured in order to map commonalities and relationships between entities. It is also populated with facts and knowledge that Google is amassing about each entity. The reason for collating all of this entity knowledge is that the vast majority of searches are not for words but for people, places and things. In addition, Keyword searches find it difficult to distinguish between words that are spelled the same way, but mean something different. Semantic

Search and Natural Language Processing systems focus meaning, the natural way humans ask and offer answers to each other, not just what they say in a few words. Natural Language is concept based which means it returns search hits on documents that are about the subject/theme being explored, even if the words in the document don’t match at all the words entered into the query. If Google has enough knowledge about all the world’s entities, it will have enough knowledge to understand what each user is searching for and answer their request more directly and accurately.

PROCESSING COSTS DECLINING FAST, WHILE THE CLOUD RISES • Cloud computing democratises big data - businesses and government agencies can now work with unstructured data at significant scale, increasing processing power and sources of information. • Competition - cloud companies keep cutting their prices, whilst they continue to increase their storage limits. • Open source technologies are ready for production use within the enterprise. This has enabled a new type of vendor that supports and packages open technologies. 1) Cloud Computing Cloud computing refers to accessing highly scalable computing resources through the Internet, often at lower prices than those required to install on one’s own computer because the resources are shared across many

Figure 1: Each new computing cycle typically generates around 10x the installed base of the previous cycle

Source: Kleiner Perkins Caufield Buyers (2014)11

Kleiner Perkins Caufield Buyers (2014) Internet Trends 2014, see www.kpcb.com/InternetTrends retrieved on December 5th 2014

Indicator of Trends Report

users. Cloud computing has become the next logical evolution in computing – combining the critical elements of each architecture that came before it. The NIST (National Institute of Standards and Technology) offers the following definition of cloud computing: “Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” Cloud computing is about the capability to access any information, at any time, regardless of the resources required and the location of the infrastructure, data, application or user. The availability of robust cloud

platforms and applications have begun to enable businesses to shift budget spending from capital expense (including dedicated, physical on-site servers) to operating expense (shared hosting by cloud providers)12. Cloud computing services are typically segmented into three different areas: i) Infrastructure as a Service (IaaS) - third-party provider hosts virtualised computing resources over the Internet, through which customers can host and develop services and applications. ii) Platform as a Service (PaaS) - used by developers to build applications for web and mobile using tools provided by the PaaS provider - these range from programming languages to databases. iii) Software as a Service (SaaS) - software is hosted in the cloud, but appears on devices with full functionality.

Figure 2: Cloud delivery options

Source: Woodside Capital Partners (2014)13

Data as a Service (DaaS) - more recently, the concept of DaaS has developed. It represents the enablement of regular, non-expert users to effectively take control of often highly complex and traditionally inaccessible IT tools.

DaaS can be defined as the sourcing, management, and provision of data delivered in an immediately consumable format to users14. Like all members of the ‘‘as a Service’’ family, DaaS is based on the concept that the product,

Woodside Capital Partners (2014) OpenStack: Is this the Future of Cloud Computing?, see www.woodsidecap.com/wp-content/uploads/2014/11/WCP-OpenStack-Report_FINALa.pdf retrieved on 21st November 2014

12-13

Data Economy

data in this case, can be provided on demand to the user regardless of geographic or organisational separation of provider and consumer. Data quality can happen in a centralised place, cleansing and enriching data and offering it to different systems, applications or users, irrespective of where they were in the organization or on the network. Figure 3: Data as a Service (DaaS)

development platforms of the future.All of these companies built a functional platform of business capabilities and extended their business models by exposing APIs so that developers can exploit their functionality. Google Maps is a key example. Many developers write mashups (using content for an application from more than one source) on top of Google Maps for various reasons, for example retail store locators, traffic reports, road conditions, and so on. Figure 4: APIs are enabling more and more devices to connect

An increasing number of Internet network owners have built their own content delivery networks (CDNs) to improve on-net content delivery and to generate revenues from content customers. For example Microsoft builds its own CDN in tandem with its own products through its Azure CDN. CDNs, like Azure and Amazon’s CloudFront, are key enablers of building, deploying and managing scalable applications with the goal of reaching a global end user audience. Through these platforms, underlying computer and storage resources scale automatically to match application demand. According to some reports, 300,000 APIs (Application Programming Interfaces) are projected to be registered by 202015. APIs are the fastest growing, business-influencing technology in the IT industry today.With an API, developers can exploit functions of existing computer programmes in other applications. Companies are exposing APIs to allow others to consume their business functions, for a profit. Where Windows and Linux have been traditional development platforms of the past, Google, Facebook, Twitter and other companies are becoming the

Source: IBM (2014)16

APIs are now coming of age with the advent of cloud computing, where the ability to host external APIs has matured to a point where cloud service providers have scalable capacity to handle transaction loads and spikes in traffic. Mobile platforms now put application reach on millions of devices, all having access to back-end APIs across the Internet. Amazon Web Services (AWS) Marketplace (Amazon’s API marketplace) attracts not only developers and partners looking to exploit Amazon’s APIs, but other vendors also, such as SAP and Oracle (that provide their own APIs on AWS, to offer analytics for additional value). 2) Competition Cloud computing is creating pricing competition that some in the industry call ‘‘the race to zero’’. A gigabyte’s worth of storage on a hard drive in 1992 cost $569, but it cost just 2 cents in 201317. Key players in the IaaS sector include Amazon, Microsoft and Google. These companies

Oracle (2014) Data-as-a-service: the Next Step in the As-a-service Journey, see www.oracle.com/us/solutions/cloud/analyst-report-ovum-daas-2245256.pdf retrieved on 2nd March 2015 15 IBM (2013) Global Technology Outlook 16 IBM (2014) Exposing and Managing Enterprise Services with IBM API Management 14

Indicator of Trends Report

keep cutting their prices, whilst they continue to increase their storage limits. Aaron Levie, CEO of the enterprise cloud storage company, Box, is quoted as saying: “We see a future where storage is free and infinite18.’’ Figure 5: Global storage cost trends

Source: Kleiner Perkins Caufield Buyers (2014)19

3) Open Source An open source community of millions of programmers build, test, and enhance software on a daily basis and anyone is free to download the components and contribute back the enhancements they develop. When Oracle pioneered the value shift from hardware to software in the mid1980s, commercial open source was virtually non-existent. In today’s world, Oracle would be expected to contribute its proprietary code back to the greater community just as vendors do in other open source projects such as Linux and Hadoop.This open source model not only lowers costs for enterprises that choose to use it but also significantly speeds up the time to market for new features and opens the door for far more innovation. This model has proven to be very successful in the Linux community, where open source Linux has become a dominant operating system20. Projects like Linux, and open systems such as the Internet, work because the original designers of the system set clear rules for co-operation and interoperability. In the case of UNIX, the original design on which Linux was based, the creators started out with a philosophy of small cooperating tools with standardised inputs and outputs that

could be assembled into pipelines. Rather than building complex solutions, they provided building blocks, and defined how anyone could write additional building blocks of their own by simply following the same set of rules.This allowed UNIX, and then Linux, to be an operating system created as an assemblage of thousands of different projects. The wide-scale adoption of Linux has significantly changed attitudes toward communitybased development. This has enabled a new type of vendor that supports and packages open technologies. Open government APIs enable a different kind of participation. When anyone can write a citizen-facing application using government data, software developers have an opportunity to create new interfaces to government. Perhaps most interesting are applications and APIs that allow citizens to actually replace functions of government. For example, FixMyStreet, a project developed by UK nonprofit, mySociety, made it possible for citizens to report potholes, broken streetlights, graffiti, and other problems that would otherwise have had to wait to be discovered. This concept has now been taken up widely by forwardthinking cities as well as entrepreneurial companies21. SENSOR

USE GROWING RAPIDLY

• Internet of things (IoT) - The fastest growing segment of valuable data comes from the IoT. Millions of networked sensors are being embedded in the physical world in devices such as mobile phones, smart energy metres, cars, and industrial machines that sense, create, and communicate data. • Citizen Science - The increased availability of open databases is an asset to the citizen science community, and a way to increase its contribution to scientific progress. • Autonomous Systems are interconnected, interactive, cognitive and physical tools, able to variously perceive their environments. They extend human capabilities, increasing our productivity and reducing our risks.

Kleiner Perkins Caufield Buyers (2014) Internet Trends 2014, see www.kpcb.com/InternetTrends retrieved on December 5th 2014 Business Insider UK (2014) see www.uk.businessinsider.com/cloud-storage-race-to-zero-2014-11?r=US retrieved on 21st November 2014 19 Kleiner Perkins Caufield Buyers (2014) Internet Trends 2014, see www.kpcb.com/InternetTrends retrieved on December 5th 2014 20 Woodside Capital Partners (2014) OpenStack: Is this the Future of Cloud Computing?, see www.woodsidecap.com/wp-content/uploads/2014/11/WCP-OpenStack-Report_FINALa.pdf retrieved on 21st November 2014 21 Lathrup, D. and Ruma, L. (2010) Open Government: Collaboration,Transparency, and Participation in Practice, O’Reilly Media 17 18

Data Economy

1) Internet of Things (IoT) The IoT connects sensors on items, products and machines, enabling users to receive a more fine-grained picture of information systems. IoT represents the next evolution of the Internet, taking a huge leap in its ability to gather, analyse, and distribute data that can be turned into information, knowledge, and actionable insights22. The IoT is forecast to reach 26 billion installed units by 2020, up from 900 million five years ago23. Whether used individually or, as is increasingly the case, in tandem with multiple devices, sensors are changing our world for the better - be it by reminding us to take our medicine, or by tracking traffic flow. Satellite imaging of weather systems, vegetation changes, and land and sea temperatures can be combined with temperature and pollution data on the ground to provide a picture of climate change and man’s impact on the planet. Limited range local sensors can provide detailed information that can be cross referenced with satellite data to validate models, which in turn can be used to provide wide area predictions and forecasts. This has been fundamental to the development of weather forecasting, and will be equally fundamental to many other satellite applications. Figure 6: Rising proliferation of devices

Source: Kleiner Perkins Caufield Buyers (2014)24

Embedding sensors in physical objects like computers, watches and robots, provides data to develop technologies that solve our needs and make business cases. For example, an imminent increase in the number of intelligent devices available is set to make supply chains smarter than ever. However it isn’t just information about the location of physical assets that will boost supply chain visibility. Data about their condition and state will be important, too. For example, if the temperature that food products are kept at throughout the supply chain can be tracked, food companies have a better chance of extending shelf-life and reducing waste25. Toyota has announced the development, in Japan, of the ‘‘Big Data Traffic Information Servic’’, a new kind of traffic-information service utilising big data including vehicle locations and speeds, road conditions, and other parameters collected and stored via telematics services. Based on such data, traffic information, statistics and other related information can be provided to local governments and businesses to aid traffic flow improvement, provide map information services, and assist disaster relief measures26. 2) Citizen Science Citizen science is scientific research conducted, in whole or in part, by amateur or non-professional scientists. Formally, citizen science has been defined as ‘‘the systematic collection and analysis of data; development of technology; testing of natural phenomena; and the dissemination of these activities by researchers on a primarily avocational basis27.” The growth of the citizen science movement represents a major shift in the social dynamics of science, in blurring the professional/ amateur divide and changing the nature of the public

Cisco (2011) The Internet of Things: How the Next Evolution of the Internet Is Changing Everything Financial Times (2014) The Connected Business, see www.im.ft-static.com/content/images/705127d0-58b7-11e4-942f-00144feab7de.pdf retrieved on 21st November 2014 24 Kleiner Perkins Caufield Buyers (2014), Internet Trends 2014, see www.kpcb.com/InternetTrends retrieved on December 5th 2014 25 Financial Times (2014) The Connected Business, see www.im.ft-static.com/content/images/705127d0-58b7-11e4-942f-00144feab7de.pdf retrieved on 21st November 2014 26 ABI/INFORM Global (2013) JAPAN:Toyota to launch Big Data Traffic Information Service 27 Wikipedia, see www.en.wikipedia.org/wiki/Citizen_science retrieved on 18th January 2015 28 The Royal Society (2012) Science as an Open Enterprise, The Royal Society Science Policy Centre report 22 23

Indicator of Trends Report

engagement with science28. There has been a shift from observing science passively to being actively engaged in the scientific discussion. Citizen science encompasses many different ways in which citizens are involved in science. This may include mass participation schemes in which citizens use smartphone applications to submit wildlife monitoring data, for example, as well as smaller-scale activities like grassroots groups taking part in local policy debates on environmental concerns over fracking. Companies are forming to promote citizen science projects. Zooniverse, a citizen science web portal owned and operated by the Citizen Science Alliance, is home to the internet’s largest collection of citizen science projects. Zooniverse has well over 1 million volunteers that can get involved in projects to participate in crowdsourced scientific research. Unlike many early internet-based citizen science projects (such as SETI@home) that used spare computer processing power to analyse data, known as volunteer computing, Zooniverse projects require the active participation of human volunteers to complete research tasks. By combining machine computing with human computing (digital volunteers), a more comprehensive analysis can be performed. Projects have been drawn from disciplines including astronomy (the Galaxy Zoo project), ecology, cell biology, humanities, and climate science. Galaxy Zoo enables users to participate in the analysis of imagery of hundreds of thousands of galaxies drawn from NASA’s Hubble Space Telescope archive and the Sloane Digital SkySurvey. It was started in 2007 by an Oxford University doctoral student, who decided to involve the community of amateur astronomers by using crowdsourcing. To understand how these galaxies formed, astronomers classify them according to their shapes. Humans are better at classifying shapes than even the most advanced computer. More than 320,000 people have now taken part in Galaxy Zoo, over 120 million classifications have been completed, and there are now more than 25 peer-reviewed publications based on data from Galaxy Zoo29. 3) Autonomous Systems A recent report by McKinsey, a consultancy firm, estimated that the application of advanced robotics could generate a potential economic impact of €1.6 trillion to €5.3 trillion, per year, by 202530. As well as satellites, Robotics and Autonomous Systems (RAS) are also one of the “Eight Great Technologies” identified by the UK Government as having the potential to drive the creation of multiple user 29 30

applications and stimulate economic growth. A RAS tool will have: • sensors that collect data from the immediate environment; • the ability to move, both itself and other things; and • software to process information, to mediate between sensory inputs, to control motion, to plan actions and to interface with people. Included in the definition of RAS are Unmanned Aerial Vehicles (UAVs). UAV and satellite data can combine to result in better solutions for a wide range of current and emerging applications including, for example, precision agriculture, environmental services and critical infrastructure monitoring. Combining satellite and UAV sources often generates complementary data that can be enriched through processing and analysis. UAVs are already making strides in civilian markets. Teal Group, an aerospace and defence consultancy, says UAV spending will nearly double over the next decade from €5 billion per annum to €9 billion. 14% of this will be accounted for by the civil market, or approximately €1.3 billion. RAS will increasingly be used to enhance almost every aspect of our lives. They will be part of our response to national challenges: an ageing population, safer transport, efficient healthcare, productive manufacturing, and secure energy. Acting as the arms and legs of big data, connected in the IoT, RAS is a ubiquitous and underpinning technology. There are identifiable hot spots where RAS capability can impact sectors including aerospace, agriculture, automotive, energy, health, manufacturing, marine, nuclear and transport. Transport encompasses road, rail and air and sea and is a critical factor in economic growth and development. In future we will be increasingly reliant on autonomous vehicles for mission-critical systems. These will need to be robust and resilient with a wide geographical coverage, and satellites will play an important role in enabling this move. CREATING VALUE

- ANALYTICAL TOOLS IMPROVING

• Intelligence as a service - Algorithms and software development creates value for the end user. Innovations in software have made it possible to store big data in an easily retrievable form, but fast and sophisticated analytical tools are required to make sense of that data. • Machine Learning - mining historical data with

Wikipedia, see www.en.wikipedia.org/wiki/Zooniverse_%28citizen_science_project%29 retrieved on 18th January 2015 Knowledge Transfer Network UK (2014) Robotics and Autonomous Systems

Data Economy

computer systems to predict future trends or behaviour and create value for users through insights. 1) Intelligence as a Service Turning raw data into knowledge through processing and analysis can lead to disruptive outputs and the creation of value for the end user. The volume, variety, velocity, and veracity of data is contributing to the increasing complexity of data management and workloads - creating a greater need for advanced analytics to discover actionable insights. Analytics means the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decision and actions. IBM outlines the following evolution of analytics31: • Descriptive: After-the-fact analytics by analysing historical data; • Predictive: Leverages data mining and statistics to analyse current and historical data to predict future events and outcomes; • Prescriptive: Synthesises big data, mathematical and computational sciences and business rules to suggest decision options; and • Cognitive: Mental processes of perception, memory, judgment, learning and reasoning. This includes natural language processing. More value is created through prescriptive and cognitive analytics than descriptive and predictive. A cognitive system is trained to extract facts from different sources of information. It compares and contrasts this evidence to make a recommendation to a professional who is in the process of making a decision. In this model, the human and machine are in a partnership. The human has the creative thought and intuition to assimilate the facts about a specific situation, develop an understanding of what is required, and make a decision on the next course of action, particularly when the information is limited. The machine has the speed to sift through large repositories of existing knowledge to locate potentially relevant information and make an assessment of it. Together, they combine the best knowledge available and apply it to a specific real world problem. 2) Machine Learning Machine learning is the science of building algorithms so that computers can recognise behaviour in large data sets. A major focus of machine learning research is to automatically identify complex patterns and make

intelligent decisions based on data. Machine learning is concerned with the design and development of algorithms that allow computers to evolve behaviours based on empirical data. Machine learning and the analytical insights it produces, creates value for the user of applications and services. Search engines, online recommendations, ad targeting, virtual assistants, demand forecasting, fraud detection, spam filters - machine learning powers all of these modern services. Steve Ballmer, former CEO of Microsoft, has said that the next era of computer science is going to focus on machine learning. “It’s a new era in which the ability to understand the world and people and draw conclusions will be really quite remarkable… It’s a fundamentally different way of doing computer science32.’’ Natural language processing (NLP) is an example of machine learning. It uses computer algorithms to analyse human (natural) language. One example application of NLP is using sentiment analysis on social media to determine how prospective customers are reacting to an advertisement campaign. Algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions. USER

INTERFACES IMPROVING TO MAKE DATA USABLE/USEFUL

• Advanced visualisations make it possible to explore and communicate the results of a wide range of data. The ultimate goal of big data visualisations is to understand and use collective knowledge of science and technology to enable anyone to explore complex technical, social and economic issues and to make better decisions. Developing the visualisation tools to handle big data images, videos and data sets remains a work in progress. Current efforts focus on ways of ensuring data quality, dealing with streaming from social media, and making tools more modular and even easier to use through gamification for example (the concept of applying game mechanics and game design techniques to better engage the user). Quality data sets are hard to obtain, and standard tools are lacking33. Consequently, initiatives have been set up, for example at Indiana University’s Cyber infrastructure

IBM (2014) Cloud, Analytics, Mobile, Social & Security (CAMSS) Presentation Computerworld (2014) see www.computerworld.com/article/2847453/ballmer-says-machine-learning-will-be-the-next-era-of-computer-science.html retrieved on 23rd November 2014 33 Credit Suisse (2013) Big Data:Taking a Quantum Leap 31 32

Indicator of Trends Report

for Network Science Centre at Indiana University. It has created an open-source, community-driven project for exchanging and using data sets, algorithms, tools and computing resources. Software toolsets have been developed that enable non-computer scientists to plug and play data sets and algorithms as easily as they share images and videos using Flickr and YouTube. Online visualisation tools such as the Twitter StreamGraph, display the frequency of keyword associations by topic over time, providing marketers with near real-time data that can be used to support product placement and development, and identify new trends34. The ability for business users to make sense of complex analytics through visual interfaces is revolutionising how decisions are made in a variety of industries. Oil and gas companies, for example, are using geographical visualisations to increase the success rate of their drilling and optimise their reservoir production. Banks and financial institutions have been among the earliest adopters of big data visualisation technology. They generate massive amounts of information as part of their daily business, and their employees are charged with making profitable decisions based on a flood of data coming in from the markets and other sources. The real power of data visualisation lies in its ability to help managers pick and choose very quickly, from a wide variety of parameters, the important elements such as risk, trading activity and position data, and then filter out less interesting elements to focus on the instruments that require closer study. Mobile devices have made technology more consumable, creating user demand for interactive tools for visual analytics. As data sets become more complex, there is a growing need for visual analytics by both business and scientific users. The use of visual analytics improves the comprehension, appreciation and trust in the analytics results and helps improve communication and collaboration around business insights. Since the tools will become more focused on domain user needs and less on visualisation and analysis experts, the tools must have high levels of interactivity, simplicity, responsiveness, and collaboration.

Credit Suisse (2013) Big Data:Taking a Quantum Leap

Data Economy

Human DISRUPTIVE

INNOVATION

Defined by Clayton Christensen, disruptive innovation points to situations in which new organisations can use relatively simple, convenient, low cost innovations to create growth and successfully compete with incumbents. The theory holds that incumbent companies have a high probability of beating entrant firms when the contest is about sustaining innovations. However, established companies almost always lose to entrants armed with disruptive innovations35. Sustaining innovations are what move companies along established improvement trajectories. They are improvements to existing products or dimensions historically valued by customers. Airplanes that fly farther, computers that process faster and cellular phone batteries that last longer are all examples of sustaining innovations. Disruptive innovations introduce a new value proposition. They either create new markets or reshape existing ones. Christensen defines two types of disruptive innovation: 1) Low-end disruptive innovations can occur when existing products and services are “too good” and hence overpriced relative to the value existing customers can use. Nucor’s steel mini-mill, Wal-Mart’s discount retail store and Dell’s direct-to-customer business model were all low-end disruptive innovations. 2) New-market disruptive innovations can occur when characteristics of existing products limit the number of potential consumers or force consumption to take place in inconvenient, centralised settings. The Sony transistor radio, Bell telephone, Apple PC and eBay online marketplace were all new-market disruptive innovations. They all created new growth by making it easier for people to do something that historically required deep expertise or significant wealth. INNOVATION

EXAMPLES

• Semiconductors Semiconductors are in just about everything. Automobiles have upwards of fifty microprocessors controlling everything from the braking system to the engine. Consumer devices such as cellular phones and DVD players rely on semiconductors. For decades the industry has been pursuing Moore’s Law – the notion that the number of transistors that can be placed on an

integrated circuit doubles approximately every two years without a corresponding increase in cost. In other words, performance doubles but costs remain the same. The semiconductor industry’s history traces back to disruptive innovation in the 1940s based on the invention of the transistor. Researchers at Bell Labs in the US developed the first transistor in 1947. The first transistors weren’t very good compared to the vacuum tubes that powered electronic devices such as TVs and table top radios. Although transistors were a lot smaller and more rugged, they could not handle the power that manufacturers of products that relied on vacuum tubes required. However the transistor’s unique properties - its small size and lower power consumption - made it a perfect tool to assist the hearing impaired and create a low-end disruption in that market. In 1952 the US company Sonotone replaced one of the three vacuum tubes that powered its hearing aid with a transistor. The next big commercial market for transistors was pocket radios, a new-market disruption. Again the unique properties of the transistor made it a perfect fit for the pocket radio. Its sound quality was initially not very good but Sony’s transistor radio created an entirely new market by competing against non-consumption - making it easier for people to do something that historically required deep expertise or significant wealth. Having a personal pocket radio opened up the market to many more potential consumers and addressed a much larger market. In recent years it has been suggested that the semiconductor industry has been “overshooting” the needs of its customers. This concept of overshooting suggests that companies try to keep prices and margins high by developing products with many more features than customers can absorb and/or actually need. This overshooting has implications. New companies can compete based on convenience and customisation. Essentially, the very things that fuelled the semiconductor industry’s success have created a circumstance in which entrants could reshape the industry. • Cloud Computing Cloud-delivered enterprise solutions fit Christensen’s concept of disruptive innovation. They offer cheaper, simpler and often more broadly applicable alternatives to legacy models of enterprise computing. They tend to start out as low-end disruptors, bringing cost and performance advantages to over-served customers, but as these technologies mature in their reliability and sophistication,

Christensen et al (2004) Seeing what’s next: using the theories of innovation to predict industry change, Harvard Business School Press

Indicator of Trends Report

they’re spreading throughout organisations and solving some of the most demanding problems36. In the 1990s, the enterprise software industry went through an upheaval as the client-server model displaced the mainframe. This new (and now old) standard represented large shift in value, because applications could now be much more powerful and modern using PC standards, data could mostly be centralised, and everything would run at a fraction of the cost compared to mainframes. Fast forward a decade and a half, and the same large-scale change has occurred yet again with most core applications being brought back to the web. Distributing technology over the web offers current market leaders no intrinsic advantage that a start-up can’t access - that is, the web is served up democratically, whereas software in the past was usually delivered via partners or vendors with the most extensive salesforces. Cloud solutions generally embrace a world defined by collaboration, mobility, and openness. Many cloud solutions today are similarly disrupting incumbents by initially slipping into the “just good enough” category. Product roadmaps then become more comprehensive and customers are served in more meaningful ways. BUSINESS

MODELS IN CLOUD COMPUTING

The following organisations are key players in the cloud computing market and in enabling a globally distributed applications infrastructure. • Microsoft Azure Microsoft Azure delivers general purpose platform as a service (PaaS), which frees up developers to focus only on their applications and not the underlying infrastructure required. Having the IT infrastructure, hardware, operating systems and tools needed to support an application opens up possibilities for developers. The Microsoft hybrid cloud leverages both on-premises resources and the public cloud. 40% of Azure’s revenue comes from start-ups and independent software vendors (ISVs), and 50% of Fortune 500 companies use Windows Azure. Microsoft has invested $15 billion to build its cloud infrastructure, comprised of a large global portfolio of more than 100 datacentres, 1 million servers, content distribution networks, edge computing nodes, and fibre optic networks37.

Leveraging Microsoft’s significant investment in infrastructure and the Azure platform, NASA was able to more easily build and operate its new “Be a Martian” site an educational game that invites visitors to help the space agency review thousands of images of Mars. Site visitors can pan, zoom and explore the planet through images from Mars landers, roving explorers and orbiting satellites dating from the 1960s to the present. In keeping with the rise of gamification, the site is also designed as a game with a twofold purpose: NASA and Microsoft hope it will spur interest in science and technology among students in the US and around the world. It is also a crowdsourcing tool designed to have site visitors help the space agency process large volumes of Mars images. Researchers at the NASA Jet Propulsion Laboratory (NASA/JPL) wanted to solve two different challenges - providing public access to vast amounts of Mars-related exploration images, and engaging the public in activities related to NASA’s Mars Exploration Programme. The sheer volume of information sent back by the rovers and orbiters is unmatched in the history of space exploration. Hundreds of thousands of detailed photographs are now stored in NASA databases, and new photos are transmitted every day. “We have so much data that it’s actually hard to process it all38.”

- Dr. Jeff Norris (2010), NASA Jet Propulsion Laboratory

The goal is to let the public participate in exploration, making contributions to data processing and analysis. It also provides a platform that lets developers collaborate with NASA on solutions that can help scientists analyse vast amounts of information to understand the universe and support future space exploration. The site was built using a variety of technologies, including the cloudbased Windows Azure platform, and Windows Azure Marketplace DataMarket - a service that lets developers and organisations create and consume applications and content on the Azure platform. The ‘Be A Martian’ site has successfully demonstrated how Web technology can help an organisation engage with a large, dispersed group of users to view graphically rich content and participate in activities that involve massive amounts of data. Using the Azure DataMarket technology and leveraging Microsoft’s cloud capacity, NASA created its experimental “Pathfinder Innovation Contest,” which is designed to harness a global pool of programming and design talent to foster more citizen science contributions to Mars exploration.

Fortune (2011) see www.fortune.com/2011/09/27/is-the-cloud-the-ultimate-disruptive-innovation/ retrieved on 20th January 2015 Microsoft website see www.news.microsoft.com/cloud/index.html retrieved on 3rd March 2015 38 Microsoft (2010) see www.microsoft.com/casestudies/Microsoft-Azure/Naspers-Pty-Ltd/New-NASA-Web-Site-Engages-Citizens-to-Help-Explore-Mars/4000008289 retrieved on 19th January 2015 36 37

Data Economy

• Amazon Web Services (AWS) Previously, large data sets such as the mapping of the human genome required hours or days to locate, download, customise, and analyse. Now, anyone can access these data sets and analyse them using, for example, Amazon Elastic Compute Cloud (EC2) instances. Amazon EC2 is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers. By hosting this important data where it can be quickly and easily processed with elastic computing resources, AWS wants to enable more innovation, at a faster pace. AWS hosts a variety of public data sets that anyone can access for free. One example of these public data sets, NASA NEX (NASA Earth Exchange), is a collection of Earth science data sets maintained by NASA, including climate change projections and satellite images of the Earth’s surface. In 2013 NASA signed an agreement with AWS to deliver NASA NEX satellite data in order “to grow an ecosystem of researchers and developers39.” Previously, it had been logistically difficult for researchers to gain easy access to earth science data due to its dynamic nature and immense size (tens of terabytes). Limitations on download bandwidth, local storage, and on-premises processing power made in-house processing impractical. Through AWS, NASA is able to leverage the existing investment already made into the platform. NASA NEX is a collaboration and analytical platform that combines state-of-the-art supercomputing, Earth system modelling, workflow management and NASA remotesensing data. Through NEX, users can explore and analyse large Earth science data sets, run and share modelling algorithms, collaborate on new or existing projects and exchange workflows and results within and among other science communities. AWS is making the NASA NEX data available to the community free of charge. ‘‘We are excited to grow an ecosystem of researchers and developers who can help us solve important environmental research problems. Our goal is that people can easily gain access to and use a multitude of data analysis services quickly through AWS to add knowledge and open source tools for others’ benefit40.’’ - Rama Nemani (2013), principal scientist for the NEX project at Ames

‘‘Together, NASA and AWS are delivering faster time to science and taking the complexity out of accessing this

important climate data41.’’ - Jamie Kinney (2013), AWS senior manager for scientific computing

Scientists, developers, and other technologists from many different industries are taking advantage of AWS to perform big data analytics and meet the challenges of the increasing volume, variety, and velocity of digital information. • Google Cloud Platform Google’s Cloud Platform is a set of modular cloud-based services that allow the user to create anything from simple websites to complex applications. Google’s vast physical infrastructure enables it to build, organise, and operate a large network of servers and fibre-optic cables. Developers are therefore building on the same infrastructure that allows Google to return billions of search results in milliseconds. Google integrates with familiar development tools and provides API client libraries. Using Google’s existing APIs and services can quickly enable a wide range of functionality for a developer’s application, including the geo-services application, Google Maps. Geo-services global revenues, made up of satellite navigation, satellite imagery, electronic maps and locationbased search, are estimated at €125 billion - €225 billion per year42. There is therefore an increasing number of software developers offering location-based services (LBS) to consumers. Google Maps APIs give developers several ways of embedding Google Maps into web pages, and allows for either simple use or extensive customisation. When Google Maps was introduced, a programmer named Paul Rademacher introduced the first Google Maps mashup (using content for an application from more than one source), HousingMaps.com, taking data from another Internet site, Craigslist.org, and creating an application that put Craigslist apartment and home listings onto a Google Map. Google hired Rademacher, and soon offered an API that made it easier for anyone to do what he did. Competitors, who had mapping APIs but locked them up behind tightly controlled corporate developer programmes, failed to seize the opportunity. Before long Google Maps had become an integral part of every web developer’s toolkit. This is unlikely to have been possible without the existing investment in cloud infrastructure and systems underlying the application. Today, Google Maps accounts for nearly 90% of all mapping mashups, versus only a few percent each for MapQuest,Yahoo!, and Microsoft, even though these companies had a head start in web mapping43.

39-40-41 NASA (2013) see www.nasa.gov/press/2013/november/nasa-brings-earth-science-big-data-to-the-cloud-with-amazon-web-services/#.VLK4KCusWSo retrieved on 19th January 2015 42 Oxera (2013) What is the economic impact of Geo-services, prepared for Google 43 Lathrup, D. and Ruma, L. (2010) Open Government: Collaboration,Transparency, and Participation in Practice, O’Reilly Media

Indicator of Trends Report

Cultural FREE

OPEN

DATA DEMAND

The demand for open government data is increasing as consumers, citizens and businesses request more information that can inform their decision making. The European Union (EU) Open Data Portal, for example, is a single point of access to a growing range of data from the institutions and other bodies of the EU. Data are free to use and re-use for commercial or non-commercial purposes. By providing easy and free access to data, the portal aims to promote the innovative use of data and enable its economic potential44.

DATA POLICIES

Data has been referred to as the new raw material of the 21st century. Like many other raw materials, it needs investment to locate, extract and refine it before it yields value. Open data, employed in combination with open platforms, such as APIs, expands the network of minds and unlocks the data’s latent potential. As a result of increased demand for access to free data, governments and agencies are doing more to open up large amounts of public sector information to the public. ESA, for example, is implementing a free, full and open data policy through the Copernicus programme of Sentinel satellites.

In 1983, President Ronald Reagan made America’s military satellite-navigation system, GPS, available to the Some types of data referred to on the UK Government’s world; entrepreneurs pounced on this opportunity. Caropen data portal, data.gov.uk, such as real-time transport navigation, precision farming and 3 million American jobs data, or crime statistics, are provided to users via open now depend on GPS46. Official weather data are also public APIs. Demand for these types of data is very high, with and avidly used by everyone from insurers to ice-cream anecdotal evidence suggesting that there are over 1 million sellers. All data created or collected by America’s federal calls on the API for London’s transport data every day45. government must now be made available free to the public, unless this Many countries have moved in the same direction. In Europe, the information would violate privacy, confidentiality held by governments could be used to generate an estimated €140 billion a or security. year48. McKinsey estimates the potential annual value to Europe’s public sector administration at €250 billion49. ‘‘Open and machine-readable is the new default for government information47.’’ Figure 7: The emerging open data ‘marketplace’ - US President, Barack Obama (2013)

Source: Deloitte (2012)50 European Union (2014), see www.open-data.europa.eu/en/about retrieved on 19th January 2015 Deloitte (2012) Open growth: Stimulating demand for open data in the UK, see www2.deloitte.com/uk/en/pages/deloitte-analytics/articles/stimulating-demand-foropen-data-in-the-uk.html retrieved on 8th February 2015 46-47-48 The Economist (2013) A new goldmine; Open data, see www.economist.com/news/business/21578084-making-official-data-public-could-spur-lots-innovation-new-goldmine retrieved on 8th February 2015 49 McKinsey Global Institute (2011) Big data:The next frontier for innovation, competition, and productivity 50 Deloitte (2012) Open growth: Stimulating demand for open data in the UK, see www2.deloitte.com/uk/en/pages/deloitte-analytics/articles/stimulating-demand-foropen-data-in-the-uk.html retrieved on 8th February 2015 44 45

Data Economy

There are lots of companies, charities and individuals who would benefit if all the data the public sector holds was shared with them, particularly if it was shared only with them. However, those benefits have to be balanced against the rights of individuals to have their data protected by government, and the risks to individuals and to society of too much data being available (for example, through making fraud easier).

APP DEVELOPMENT In The Cathedral & the Bazaar, a book by Eric Raymond (2001) on software engineering methods, the image of a bazaar is used to contrast the collaborative development model of open source software with traditional software development. In the traditional software development “vending machine model”, the full menu of available services is determined beforehand. A small number of vendors have the ability to get their products into the machine, and as a result, the choices are limited, and the prices are high. A bazaar, by contrast, is a place where the community itself exchanges goods and services51. In the technology world, the equivalent of a thriving bazaar is a successful platform. In the computer industry, the innovations that define each era are frameworks that enabled a whole ecosystem of participation from companies large and small. The personal computer was such a platform and so was the World Wide Web. This same platform dynamic is playing out now in the recent success of the Apple iPhone. Where other phones have had a limited menu of applications developed by the phone vendor and a few carefully chosen partners, Apple built a framework that allowed virtually anyone to build applications for the phone, leading to an explosion of creativity, with more than 100,000 applications appearing in little more than 18 months, and more than 3,000 new ones now appearing every week52. Android, with a global smartphone operating system market share of around 80%53, is open-source software for a wide range of mobile devices and a corresponding open-source project led by Google. These successes are due to the openness around frameworks. As applications move closer to the mass of data, for example by building an applications ecosystem around free and public data sets, more data is created. This concept of ‘data gravity’ is about reducing the cycle time/feedback

loop between information and the data presented. This is achieved through lower latency and increased. There is an accelerative effect as applications move closer to data. Smartphones and tablets, collectively ‘mobile devices’, are the fastest adopted technology in history. They have been adopted faster than cell phones, personal computers (PCs), televisions, even the Internet and electricity. The reason why the likes of Apple (iOS) and Google (Android) lead the way in mobile applications is because they combine a large pool of talented mobile developers with a robust development infrastructure. Apple ignited the app revolution with the launch of the App Store in 2008, and since then, an entire industry has been built around app design and development. According to recent announcements from Apple, apps on iOS generated over €8 billion in revenue for developers in 2014 and to date, App Store developers have earned a cumulative €20 billion from the sale of apps and games54. According to Flurry Analytics, a mobile analytics firm, in 2014 overall app usage grew by 76%. DATA

PRIVACY AND BIG DATA

All signs seem to point to a further surge in gathering, storing, and reusing our personal data55. The size and scale of data collections will increase as storage costs continue to decline and analytical tools become more powerful. Handled responsibly, big data is a useful tool of rational decision-making. Today’s consumers are eager for companies to deliver exciting, personalised services. For the most part, they are willing to share personal information to get it.The majority of consumers have accepted the fact that companies collect and use their personal information, sharing basic information such as names, addresses, gender, and home phone numbers. Consumers also want companies to be transparent about what information they collect and how it will be used. Above all, consumers want to be in control of their personal information56. As an ever larger amount of data is digitised and travels across organisational boundaries, there is a set of policy issues that will become increasingly important, including, but not limited to, privacy, security, intellectual property, and liability. Privacy is an issue whose importance, particularly to consumers, is growing as the value of big data becomes

Lathrup, D. and Ruma, L. (2010) Open Government: Collaboration,Transparency, and Participation in Practice, O’Reilly Media Business Insider website, see www.businessinsider.com/iphone-v-android-market-share-2014-5?IR=T retrieved on 3rd March 2015 54 Apple (2015) see www.apple.com/pr/library/2015/01/08App-Store-Rings-in-2015-with-New-Records.html retrieved on 19th January 2015 55 Mayer-Shonberger, V. and Cukier, K (2013), Big Data: A Revolution 56 PWC (2012) The speed of life: Consumer intelligence services, see www.pwc.com/us/en/industry/entertainment-media/assets/pwc-consumer-privacy-and-information-sharing.pdf retrieved on 18th January 2015 51-52 53

Indicator of Trends Report

more apparent. Personal data such as health and financial records are often those that can offer the most significant human benefits, such as helping to pinpoint the right medical treatment or the most appropriate financial product. However, consumers also view these categories of data as being the most sensitive. It is clear that individuals and the societies in which they live will have to deal with trade-offs between privacy and utility. Another closely related concern is data security, for example how to protect competitively sensitive data or other data that should be kept private. Recent examples have demonstrated that data breaches can expose not only personal consumer information and confidential corporate information but even national security secrets. With serious breaches on the rise, addressing data security through technological and policy tools will become essential. Big dataâ&#x20AC;&#x2122;s increasing economic importance also raises a number of legal issues, especially when coupled with the fact that data are fundamentally different from many other assets. Data can be copied perfectly and easily combined with other data. The same piece of data can be used simultaneously by more than one person. All of these are unique characteristics of data compared with physical assets. Questions about the intellectual property rights attached to data will have to be answered also. Users entrust their most sensitive, private, and important information to technology companies. These companies are privy to the conversations, photos, social connections, and location data of almost everyone online. The choices these companies make affect the privacy of every one of their users. In the wake of recent revelations about mass surveillance by governments, independent organisations like the Electronic Frontier Foundation (EFF) have filed constitutional challenges to mass surveillance programmes. The EFF also examines the publicly-available policies of major Internet companies- including Internet service providers, email providers, mobile communications tools, telecommunications companies, cloud storage providers, location-based services, blogging platforms, and social networking sites - to assess whether they publicly commit to user privacy policies and the protection of user data.

EO Market Trends

EO MARKET TRENDS The rest of this report will focus on the key drivers of change impacting the EO industry, including processing, distributing and creating insights from EO data.

Figure 8: Historical trend in ESA’s EO data archives (1986-2010) and future projections

Technological VOLUME

OF DATA INCREASING

Operational commercial EO satellites were collecting more than 4 million square kilometres of imagery every day, as recently as 201357. In the next five years ESA spacecraft alone will obtain about 25 petabytes of EO data58 as a result of the Copernicus programme. Source: ESA (2013)59

Some of the key trends driving the significant increase in volume of data include:

Figure 9: Number of civilian EO near-polar orbiting satellites launched per year

• Improvements in mission technology – more EO satellites are being launched by developed and emerging space nations, with increased reliability of EO satellite missions contributing to more launches and an upsurge in the volume of EO data collected. • Trend towards small satellites – by bringing the cost down and accessibility up, small satellite technologies are lowering the barriers for new entrants to the EO market. Commercial small satellite business models are attracting private funding. 1) Improvements in mission technology The number of satellites orbiting the Earth with an EO mission has increased as a function of reliability and mission longevity60, with the average number of satellites launched increasing from approximately 2 in the 1970s to 12 in 2012. The number of failing missions within 3 years of launch has dropped since the 1970s from around 60% to less than 20%, resulting in an extended average mission operational lifespan that is almost 3 times higher today, increasing from 3.3 years to 8.6 years61. 57 58

Note: The horizontal dotted line represents the average number of launches per decade, respectively: 2 - 1970s; 2.7 – 1980s; 4.8 – 1990s; 7.4 – 2000s; 12 – 2010s Source: Belward and Skøien (2014), in International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

Navulur et al (2013) Demystifying Cloud Computing for Remote Sensing Applications, Earthwide Communications LLC Di Meglio et al., (2014) CERN Openlab Whitepaper on Future IT Challenges in Scientific Research, see www.zenodo.org/record/8765# retrieved on 9th February 2015

Indicator of Trends Report

Today there are around 35 sovereign states that own and/or operate their own EO satellites, with the leading space agencies (NASA, ESA and the Japan Aerospace Exploration Agency (JAXA)) still playing a pivotal role in this sector.The US has the highest number of EO missions (almost one third of all the successful missions to date), followed by Russia (14%). New players are also emerging, such as Algeria, Malaysia, Nigeria, Turkey, Thailand, South Africa and Vietnam62. Figure 10: Selected on-going and planned institutional EO missions by civilian agencies

on increasing the supply of quality EO data, is driving a forecast of 2,000-2,750 small satellites to be launched from 2014-2020, more than four times the number launched in the 2000-2012 period63. Through constellations of small satellites, new entrants like Planet Labs aim to image the entire Earth, daily. The company, which operates the largest ever fleet of earth-imaging satellites64, has already launched 71 nano-satellites to date65. Miniaturisation of technology, standardisation and reduced launch costs are driving increased interest in small, micro and nano-satellites66. The most likely domain to be disrupted by the small mission technology trend is EO over half of future nano/microsatellites will be used for EO and remote sensing purposes (compared to 12% from 2009 to 2013), with the commercial sector contributing 56% of nano/microsatellites over the next three years67. Figure 11: Nano and micro-satellite trends by purpose

Source: OECD calculations based on Committee on Earth Observation Satellites (CEOS) (2013)

2) Trend towards small satellites The adoption of constellations by new businesses intent

Source: SpaceWorks (2014)68

59 ESA (2012) Long term data preservation: status of activities and future ESA programme, see www.earth.esa.int/gscb/papers/2012/8-LTDP_activities_future.pdf retrieved on 10th February 2015 60-61 Belward and SkĂ¸ien (2014) Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites, in International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) 62 Jayaraman, V. (2014) Global Perspective on Earth Observation: Emerging Trends & Policy Challenges, Indian Space Research Organisation (ISRO), see www.ucsusa.org/ nuclear_weapons_and_global_security/solutions/space-weapons/ucs-satellite-database.html#.VLfuHyusVUU retrieved on 9th February 2015 63 SpaceWorks (2014) Trends in Average Spacecraft Launch Mass 64 Planet Labs website (2014) see www.planet.com/solutions/ retrieved on 9th February 2015 65 Planet Labs website (2014) see www.planet.com/pulse/watch-28-satellites-launch/ retrieved on 10th February 2015 66 Generally accepted definition of satellite classes: small-satellites from 500kg to 100kg, micro-satellites from 100kg to 10kg, nano-satellites from 10kg to 1kg, pico-satellites less than 1kg 67-68 SpaceWorks (2014) Trends in Average Spacecraft Launch Mass

EO Market Trends SATELLITE

CAPABILITIES IMPROVING

Improvements in on-board sensors are driving the increase in variety and quality of data from EO satellites. Since the 1970s there has been an improvement in spatial resolution from around 80 metres to less than 1 metre for multispectral, and less than half a meter for panchromatic69. Synthetic Aperture Radar (SAR) resolution has also improved, from 25 metres in 1978 to 25 centimetres in 201370. Recent developments have allowed EO optical satellites to reach sub-meter resolution with Surrey Satellite Technology Limited’s (SSTL) 300 S1 platform71. More capable on-board storage, faster downlink and better reliability are characteristics of satellites being built today. Examples that highlight these trends include: • modularity and open-architecture for expansion of existing clusters of satellites; • inter-satellite connectivity advancements are pinpointing distributed on-board processing and advanced data routing protocols enhancement; and • reconfigurable on-board electronics enable upgrades to the satellite’s software system after launch has occurred72. Data Timeliness - Having timely access to imagery from the Sentinel-1 mission, for example, is essential for numerous applications such as maritime safety and helping to respond to natural disasters. Orbiting from pole to pole about 700 km up, Sentinel-1A transmits data to Earth routinely, but only when it passes over its ground stations in Europe. However, geostationary satellites, hovering 36,000 km above Earth, have their ground stations in permanent view so they can stream data to Earth all the time. Creating a link between the two kinds of satellites means that more information can be streamed to Earth, and almost continuously. Engineers have turned to laser to accomplish this. Marking a first in space, Sentinel-1A and Alphasat have linked up by laser stretching almost 36,000 km across space to deliver images of Earth just moments after they were captured. This important step demonstrates the potential of Europe’s new space data highway to relay large volumes of data very quickly so that

information from Earth-observing missions can be even more readily available. A number of on-board EO satellite technologies are improving the quality of data collected, including: • Synthetic Aperture Radar (SAR) - Synthetic Aperture Radar (SAR) is a radar system that generates high-resolution remote sensing imagery. Their higher weather resilience make SAR sensors particularly suitable for environmental monitoring. • Multispectral and Hyperspectral sensors Multispectral and Hyperspectral sensors involve the acquisition of visible, near infrared, short-wave infrared and thermal infrared images in several broad wavelength bands. Their capability to distinguish specific materials’ characteristics allow them to be used in applications ranging from mineralogy to farming. • Deployable optics - Deployable optics can extend the satellite optical payloads’ capabilities beyond the wavelength limit allowed by the satellite’s physical dimensions. 1) Synthetic Aperture Radar (SAR) SAR is an active microwave sensor that can provide images of the earth at high spatial resolutions. The microwave region of the electromagnetic spectrum is defined by wavelengths ranging from 1 centimetre to 1 metre73. SAR data provides, in simplified terms, all-day, “all-weather”74 imaging capabilities. SAR sensors can operate irrespective of cloud cover and day light exposure - two considerable limitations of EO optical sensors. SAR data can be utilised in a number of applications, including75: • Precision agriculture • Maritime surveillance • Disaster monitoring and early detection • Glacier, sea, lake and river ice monitoring; and • Cartography. 2) Multispectral and Hyperspectral sensors Multispectral sensors acquire visible, near infrared, shortwave and thermal infrared images in several broad

Belward and Skøien (2014) Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites, in International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) 71 Surrey Satellite Technology Ltd (SSTL) SSTL 300 S1, see www.sstl.co.uk/Downloads/Datasheets/SSTL-300-S1-Datasheet-pdf retrieved on 9th February 2015 72 Frost & Sullivan (2014) Space Mega Trends, Key Trends and Implications to 2030 and Beyond 73 Sengupta, M. and Dalwani, R. (2008) Potential Applications of Multi-Parametric Synthetic Aperture Radar (Sar) Data in Wetland Inventory, see www.academia. edu/9024355/Potential_Applications_of_Multi-Para_metric_Synthetic_Aperture_Radar_Sar_Data_in_Wetland_Inventory_A_Case_Study_of_Keoladeo_National_ Park_A_World_Heritage_and_Ramsar_Site_Bharatpur_India retrieved on 9th February 2015 74 Noting that certain weather conditions, such as severe thunderstorms, can cause losses of data in the shorter wavelengths (e.g. x-band) 75 Holecz, F. et al (2014) Land Applications of Radar Remote Sensing, see www.intechopen.com/books/land-applications-of-radar-remote-sensing retrieved on 5th February 2015. 76 ESA website (2014) see www.esa.int/Our_Activities/Space_Engineering_Technology/Hyperspectral_imaging_by_CubeSat_on_the_way retrieved on 4th February 2015 69-70

Indicator of Trends Report

Figure 13: Current and planned EO missions with Hyperspectral data capabilities wavelength bands to reveal ‘spectral signatures’ of particular features, such as crops or materials76. Hyperspectral sensors can enhance these capabilities by ‘dividing up’ the light they receive into over one hundred contiguous spectral bands. As materials absorb and then Source: Northern Sky Research (2013) reflect at different wavelengths, multispectral and hyperspectral 3) Deployable optics sensors can identify and distinguish between materials Deployable optics provide the benefits of a satellite optical by detecting their spectral reflectance signatures. With payload at a much longer wavelength than is permitted by commercial high-resolution satellites that can measure the a satellite’s physical dimensions.This technology can extend Earth in sixteen optical bands, there are opportunities to the capabilities of the small satellite segment. Research provide valuable data for mineral exploration, precision 77 and development activities have started to demonstrate farming and agricultural support services . A number that this capability can be extended with the adoption of of planned EO missions in the coming years will make optical mirror segments and metering structures79. multispectral and hyperspectral data available, thus expanding their potential for providing value-added EO services78. Figure 14: 3U CubeSat with deployable optics payload Figure 12: Multispectral sensors on board near-polar orbiting EO civilian satellites, per year

Source: UK Astronomy Technology Centre (STFC) (2014)

DATA

Source: Belward and Skøien (2014), in International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

SOURCES DIVERSIFYING

End users of downstream applications and services are becoming more data source agnostic than ever before and are exposed to ever larger data sets. At the same time the innovation in cloud-computing, which offers unprecedented capabilities for large data processing and analysis, is creating the opportunity to capture increased volume and intelligence from the data. Combining satellite and UAV sources, for example, often generates complementary data that can be enriched through processing and analysis.

Sabins, F. (1999) Remote sensing for mineral exploration, Ore Geology Reviews, see www.sciencedirect.com/science/article/pii/S0169136899000074 retrieved on 3rd February 2015 78 Northern Sky Research (2013) Satellite-Based Earth Observation, 5th Edition 79 Champagne et al (2014) CubeSat Image Resolution Capabilities with Deployable Optics and Current Imaging Technology, see www.digitalcommons.usu.edu/cgi/ viewcontent.cgi?article=3059&context=smallsat retrieved on 2nd February 2015 77

EO Market Trends

To illustrate this point, the Facebook-backed not-for-profit, internet.org, aims to change the way in which the internet is distributed, so that the 2/3 of the world’s population that don’t currently have internet access can be included and reap the benefits that the internet brings. Through its Connectivity Lab, Facebook is investigating using a combination of satellites, solar powered UAVs and lasers to tie these solutions together. In early 2014, Facebook acquired UK based SME Ascenta, for a reported $20m80, to employ the company’s expertise in developing high altitude solar powered UAV technology and thereby strengthen its internet.org plan81. In the EO market end users and processers of data are increasingly agnostic in terms of the data source, so long as it meets their requirements. The combination of satellite, UAV and other aerial sources can provide large volumes of data that can be cross-referenced and utilised for more efficient analysis and application. There is a role for UAVs to play in the EO market given their more agile tasking ability and potential for persistent monitoring in comparison with satellites. The miniaturisation of remote sensing instruments is converging with the increasing payload capabilities of the UAV market, enabling a whole new range of applications and services for markets like disaster relief, agriculture, mining, mobile and fixed communications and maritime surveillance. UAVs can provide greater spatial resolution than satellites, have a shorter data delivery lag and their payload capabilities are becoming more flexible82. UAV imaging capabilities are also improving in terms of SAR and hyperspectral imaging sensors. “This combined UAV and satellite approach will be a game-changer” - Sam Adlen (2015), Head of Business Innovation, Satellite Applications Catapult

Financial Times (2014), see www.ft.com/cms/s/0/b8a6524a-b627-11e3-b40e-00144feabdc0.html?siteedition=uk#slide0 retrieved on 6th January 2015 Frost & Sullivan (2013) Emerging Applications for Unmanned Aerial Systems (UAS) Across Global Government and Commercial Sectors UAS Platforms are Flexible and Hold Great Potential 80

81-82

Indicator of Trends Report

Human NEW BUSINESS MODELS GENERATING

ACTIONABLE INSIGHTS New entrants to the EO sector, including Skybox Imaging, Planet Labs and Spire, are opening their data to developers and end-users through APIs. APIs can make it easier to access EO data and to extract the embedded value. Planet Labs has announced that it will release a developer API this year83, while Skybox Imaging is already allowing external algorithms to run on its data and infrastructure by downloading a Software Development Kit84. Business models are also emerging to develop a more integrated network of stakeholders. CloudEO, a German company that supplies EO data on a payper-use or subscription basis85, aims to bring together imagery providers, analytics companies and customers through one platform. In order to attract and expand the user community beyond the boundaries of EO, the development of semantic search structures can play a pivotal role in reaching new users. The GEOinformation for Sustainable Development Spatial Data Infrastructure (GEOSUD SDI) is one example of this86. Among the new products and services that are being developed, EO video data products are worth highlighting. Enabled by more frequent revisit times of EO satellite constellations, these products have the potential to improve the value proposition of a satellite data provider in applications such as disaster relief, surveillance and other applications that could benefit from real-time monitoring87. Canadian company, UrtheCast, has been granted the exclusive right to operate two cameras on the Russian module of the International Space Station (ISS)88. As the ISS passes over the Earth, UrtheCast’s twin cameras capture and download large amounts of HD (5 metre resolution) video and photos. This data is then stored and made available via APIs on the basis of a payfor-use model89. One of the innovative characteristics of UrtheCast’s business model is the way it approaches the revenue streams it can tap into, for example by providing

videos free of charge and generating an online advertisinglike revenue from companies that will have their logos featured on the video in relation to their locations90. THEMATIC

PLATFORMS

The platform model creates a community and within this user community it is possible to leverage the combined effort of a network of developers.The focus on leveraging existing cloud infrastructure investment and making it easier to discover, access and process EO datasets in the cloud will help to drive increased adoption of EO data in applications. A paradigm shift has occurred in the way big data is processed and distributed - now the “computations” move to the “data”, and not the data to the users. In return, the new way of exploiting big data is driving new requirements and innovation for the data infrastructure. Such technologies are leading to the emergence of an ecosystem of Thematic Exploitation Platforms (TEPs), capitalising on co-location of computing resources and data storage, such as the Super Site Exploitation Platform (SSEP) in ESA. The platform was developed in close collaboration with, among others, NASA, JAXA and the National Science Foundation (NSF). The aim is to provide tools and services that can help the EO community in exploiting EO data, assist researchers in developing applications and facilitate service providers in generating value added information91. The Data Cube in Australia is another example of a TEP. It provides Landsat,Terra (and soon Sentinel data) that dates back to 1979 and covers the whole of Australia and some surrounding countries. It is a way of organising satellite data and ensuring every pixel and image is a measurement of change over time. The Data Cube puts EO data into a high performance computing (HPC) environment where it can be accessed and used more effectively. Users can do their work on the data via the platform, rather than downloading and taking copies of the data. The aim is to move away from data processing and preparation for users and put that in the power of the user through creation of algorithms, for example.

Planet Labs website, see www.planet.com/flock1/ retrieved on 29th January 2015 Skybox Imaging website, see www.skyboximaging.com/products/analytics retrieved on 29th January 2015 85 Henry, C. (2014) CloudEO Starts ‘Virtual Constellation’ Access with Beta Online Marketplace, see www.satellitetoday.com/technology/2014/03/26/cloudeo-starts-virtual-constellation-access-with-beta-online-marketplace/ retrieved on 28th January 2015 86 M. Kazmierski et al (2014) GEOSUD SDI: accessing Earth Observation data collections with semantic-based services, see www.agile-online.org/Conference_Paper/cds/ agile_2014/agile2014_138.pdf retrieved on 19th January 2015 87 Northern Sky Research (2013) Satellite-Based Earth Observation, 5th Edition 88 UrtheCast (2013), see www.investors.urthecast.com/interactive/lookandfeel/4388192/UrtheCast-Investor-Deck.pdf retrieved on 29th January 2015 89 IAC (2014) UrtheCast is #DisruptiveTech, Onwards and Upwards Blog, see www.blog.nicholaskellett.com/2014/10/03/iac-2014-urthecast-is-disruptivetech/ retrieved on 19th January 2015 90 UstreamTV (2012) UrtheCast Business Model, see www.ustream.tv/recorded/26973814 retrieved on 19th January 2015 91 Loekken, S. and Farres, J. (2013) ESA Earth Observation Big Data R&D Past, Present, & Future Activities, Earth Observation Programmes Directorate, ESA 83 84

EO Market Trends

More is being done to pool EO resources. The Group on Earth Observations (GEO) is a voluntary partnership between 90 countries, the European Commission, and 77 intergovernmental, international, and regional organisations that share EO data and science92. This partnership has combined efforts to build a Global Earth Observation System of Systems (GEOSS), which pools several EO datasets, making them available online93. EO VALUE

CHAIN - SECTOR SEGMENTATION

Figure 15: EO market size by segment

In 2012, the global space-based EO market was estimated to be worth €1.8 billion94. The segment with the greatest potential is valueadded/information products. A report by Northern Sky Research has forecast that this area will grow to €2.5 billion between 2012 and 2022. The EO data sales segment will represent less than a third of the EO sector’s overall global revenues, while information products will represent more than half of the total95. Source: Northern Sky Research (2013)

This forecast is in line with recent developments in the EO sector, namely: Figure 16: EO sector market verticals: value projection to 2022

Source: Northern Sky Research (2013)

92-93 94-95

Group on Earth Observation website, see www.earthobservations.org/geoss.php retrieved on 29th January 2015 Northern Sky Research (2013) Satellite-Based Earth Observation, 5th Edition

• volume and quality of EO datasets is increasing; • data processing and storage capabilities are improving both in terms of performance and affordability; and • proliferation of devices and sensors is expanding the range of data sources. The EO sector has delivered value to a large array of market verticals, ranging from Defence and Intelligence to Living Resources, and several sources are predicting sustained growth in these markets.

Indicator of Trends Report

2030100. Lessons can also be learned from the Brazil-China CBERS and US Landsat programmes, both of which have shown that after distributing the datasets free for several ACCESS TO DATA POLICY years, strong user growth has been achieved101. According to a 2013 U.S. Geological Survey (USGS) report, in 2011, “Data can be equated with money that has value only if the economic benefit from Landsat imagery obtained it is used and circulated.96” from EROS (Earth Resources - Partnership for Advanced Data in Europe (PARADE) (2009) Observation and Science) was estimated to be just over $1.79 billion for U.S. users and almost EO data is no different to other data sources - if not used and circulated their $400 million for international users, value cannot be captured fully. resulting in a total annual economic benefit of $2.19 billion. This estimate does not include benefits There is a trend towards an open data sharing policy and from reuse of the imagery after it has been obtained from certain organisations are naturally positioned to collect EROS or from the use of value-added products based on and store large volumes of data, for example public sector Landsat imagery. bodies. These organisations have to produce, collect and

Cultural

distribute a large variety of information, ranging from business and economic data and statistics, to geographic and meteorological information.

Copernicus programme The Copernicus programme (formerly known as GMES) is a European system for monitoring the Earth, co-ordinated and managed by the European Commission. It consists of a complex set of systems that collect data from multiple sources, including EO satellites, in situ sensors (e.g. ground stations, airborne and sea-borne sensors)97. It will ensure the regular observation and monitoring of our planet and will provide users with reliable and up-to-date information via services that address six thematic areas: land, marine, atmosphere, climate change, emergency management and security. Datasets will be open and accessible to any citizen with one of the main objectives of the programme being98:

Landsat programme The Landsat programme provides the longest continuous space-based record of Earth’s land in existence. Since 2008 imagery has been made available free of charge to users and the rate of downloads has increased significantly as a result102. Figure 17: Landsat images downloaded from USGS EROS Centre (cumulative data)

“To create massive business opportunities for European companies, in particular SMEs, to boost innovation and employment in Europe99” SpaceTec analysed the potential impact of the Copernicus programme on the EO midstream sector (defined as the market segment made up of companies that sell or distribute EO data) and concluded that there would be between €560 million and €635 million worth of positive externalities on the employment market during 2014-

Source: Northern Sky Research (2013)

Koski, K. et al (2009) Strategy for a European Data Infrastructure:White Paper, see www.grdi2020.eu/Pages/SelectedDocument.aspx?id_documento=a852120d-5f204fce-b758-0416c01fbb56 retrieved on 6th January 2015 97 ESA website, see www.copernicus.eu/pages-principales/overview/copernicus-in-brief/ retrieved on 9th February 2015 98 ESA website, see www.copernicus.eu/pages-principales/overview/faqs/ retrieved on 9th February 2015 99 European Commission (2012) GMES/Copernicus – monitoring our earth from space, good for jobs, good for the environment, see www.europa.eu/rapid/press-release_ MEMO-12-966_en.htm retrieved on 2nd February 2015 100 SpaceTec Partners (2013) European Earth Observation and Copernicus Midstream Market Study 101 Northern Sky Research (2013) Satellite-Based Earth Observation, 5th Edition 102 National Geospatial Advisory Committee Landsat Advisory Group (2014) The Value Proposition for Landsat Applications, see www.fgdc.gov/ngac/meetings/december-2014/ngac-landsat-economic-value-paper-2014-update.pdf retrieved on 9th February 2015 96

EO Market Trends

The cumulative savings achieved are estimated to be • very high resolution (VHR) (sub-metre); between $350 million and $436 million per year for • high resolution (HR) (between 1 metre and 2.5 Federal and State governments, NGO’s and the private metres); and sector103. More EO data available leads to greater • medium resolution (MR) and low resolution (LR) use of datasets, which in turn enables developers and (above 2.6 metres). entrepreneurs to approach data analysis from different and previously unexplored perspectives. The Climate The trend towards increased availability of free EO data Corporation (Climate Corp), recently acquired by global through programmes like Landsat and Copernicus will 104 agri-company Monsanto for close to $1 Billion , sells mainly address the MR and LR segments, with commercial customised weather insurance policies to US farmers operators focusing on the HR and VHR data market107. alongside a number of advisory services aimed at increasing crop yields. Climate Figure 18:Average price per square km of main commercial EO products Corp’s development started from the and Copernicus programme impact collection of freely available datasets, including Landsat images and topography maps from the US Geological Service105. DATA

PRICE REDUCTION

Commercially available EO data has been experiencing a sustained decrease in price over time and this trend is expected to continue. One of the main drivers of this price reduction is growth in the supply of EO data - determined by the increased number of EO missions launched worldwide, free and open data policies and new market entrants. Planet Labs, for example, will offer a tiered pricing system as well as a ‘freemium’ model. In an interview with Forbes in 2014, Robbie Schingler, cofounder of Planet Labs said:

Source: EARSC (2012) – Public company data, STP analysis

“If you are a small farmer in Mozambique and you’re only looking at a hectare of data, take it. If you are a commodity trader looking at the Kenyan crop and you’re looking at millions of hectares, we have a different pricing structure. This allows us to ensure the democratisation of data106.” EO data pricing itself is determined by a number of different factors, including: • technical parameters and capabilities of data - ground and spectral resolutions, revisit time, accuracy and geographic area coverage; • region of data sale - commercial operators can decide to apply different data pricing policies in different regions. Price dynamics can be better analysed when categorised into three main resolution segments:

High Resolution (HR) and Very High Resolution (VHR) In June 2014 the US Government announced that restrictions on EO data sales were to be lifted - imagery from satellites with a resolution of sub-50cm can now be sold108. Recent analysis estimates the EO data segment will grow by close to €780 million in the next 10 years and that most of this growth will come from the HR segment, accounting for more than €740 million109.The HR segment has grown strongly in the last few years, its global market share within the EO data market increased from 58.9% in 2007 to 74.8% in 2012110. HR EO data prices are expected to remain relatively high (SAR data in particular)111. Figure 19: HR data price forecasts

Note: prices are indicated as $ per sq km. Source: Northern Sky Research (2013)

Indicator of Trends Report

Figure 20: EO HR data price expectation to 2022

Source: Northern Sky Research (2013)

Low Resolution (LR) It is expected that free access to EO data will have the most impact on this segment of the EO data market. Commercial value will be determined by the capability of the private players in this segment to offer additional services. New players using small satellite constellations are aiming to differentiate their offering with more frequent revisits. They are challenging existing revisit times offered by EO commercial operators and moving from an existing multi day revisit time to a daily revisit time.These new market entrants include Skybox Imaging with its SkySat satellite and Planet Labs, with its Flock satellite constellation.

Figure 21: EO satellites: spatial resolution vs. revisit time

Source: Satellite Applications Catapult (2014)

National Geospatial Advisory Committee Landsat Advisory Group (2014) The Value Proposition for Landsat Applications, see www.fgdc.gov/ngac/meetings/december-2014/ngac-landsat-economic-value-paper-2014-update.pdf retrieved on 9th February 2015 104 Forbes (2013) see www.forbes.com/sites/bruceupbin/2013/10/02/monsanto-buys-climate-corp-for-930-million/ retrieved on 2nd February 2015 105 University of Washington (2014) The Climate Corporationâ&#x20AC;&#x2122;s hyper-local weather insurance, see www.conservationmagazine.org/2014/07/the-climate-corporations-hyper-local-weather-insurance/ retrieved on 30th January 2015 106 Forbes website (2014), This Radical Satellite Startup is Democratizing Space, see www.forbes.com/sites/joshwolfe/2014/04/02/this-radical-satellite-startup-is-democratizing-space-video/ retrieved on 17th March 2015 107 Euroconsult (2012) Satellite-Based Earth Observation: Market Prospects to 2021 108 BBC News (2014) see www.bbc.co.uk/news/technology-27868703 retrieved on 9th February 2015 109-110-111 Northern Sky Research (2013) Satellite-Based Earth Observation, 5th Edition 103

Conclusions

CONCLUSIONS • Increasing value is created as data progresses through the value chain. Government agencies are working with private industry to stimulate innovative applications from the data. • More data is being generated than ever before through the Internet of Things, information layers and diversified sources. An increased number of public and private (including new commercial players) EO satellite missions are contributing to an upsurge in EO data captured, with the intention of distributing this data to enable the creation of innovative applications. • The cloud computing revolution continues to put downward pressure on the cost of processing big data, enabling larger datasets to be used. New entrants and free data access are changing price points in some segments of the EO data market. The quality and accuracy of EO data captured is improving through better on-board technology and a wider range of sensors. • Turning the raw data into knowledge and actionable insights is where value is created. APIs, algorithms and open thematic platforms aim to extract value from raw data and turn pixels into insights. • Democratising data use will engage a wider user community, therefore increasing the likelihood of stimulating new, disruptive innovations. To unlock the economic potential of this data, agencies are creating products that are responsive to user needs and implementing free and open data policies, including ESA’s Copernicus Programme. • Agencies have a key role to play as stewards of big data and are actively engaged in analysing how to best create a healthy, stable data ecosystem, including investigating Public Private Partnerships to unlock the economic potential of agency data, and take advantage of industry’s extensive distribution platforms and cloud computing infrastructure.

EO21: Earth Observation Market in the 21st Century. Indicator of Trends Report

Articles inside

EXECUTIVE SUMMARY

DATA ECONOMY

EO MARKET

CONCLUSIONS