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ISSI Scientific Report 15
Pierre-Philippe Mathieu
Christoph Aubrecht
Editors
Earth Observation
Open Science
and Innovation
ISSI Scientific Report Series
Volume 15
The ISSI Scientific Report Series present the results of Working Groups (or Teams)
that set out to assemble an expert overview of the latest research methods and
observation techniques in a variety of fields in space science and astronomy. The
Working Groups are organized by the International Space Science Institute (ISSI)
in Bern, Switzerland. ISSI’s main task is to contribute to the achievement of a
deeper understanding of the results from space-research missions, adding value to
those results through multi-disciplinary research in an atmosphere of international
cooperation.
More information about this series at http://www.springer.com/series/10151

Pierre-Philippe Mathieu • Christoph Aubrecht
Editors
Earth Observation Open

Science and Innovation
Editors
Pierre-Philippe Mathieu Christoph Aubrecht
ESA/ESRIN ESA/ESRIN & World Bank
Frascati, Italy Washington, DC, USA
ISSI Scientific Report Series

ISBN 978-3-319-65632-8 ISBN 978-3-319-65633-5 (eBook)
https://doi.org/10.1007/978-3-319-65633-5
Library of Congress Control Number: 2017958445
© The Editor(s) (if applicable) and The Author(s) 2018. This book is an open access publication.
Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 Inter-
national License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation,
distribution and reproduction in any medium or format, as long as you give appropriate credit to the
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The registered company is Springer International Publishing AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Foreword
The world of Earth Observation (EO) is rapidly changing as a result of exponential

advances in sensor and digital technologies. The speed of change has no historical
precedent. Recent decades have witnessed extraordinary developments in ICT,
including the Internet, cloud computing and storage, which have all led to radically
new ways to collect, distribute and analyse data about our planet.
This digital revolution is also accompanied by a sensing revolution that provides
an unprecedented amount of data on the state of our planet and its changes.
Europe leads this sensing revolution in space through the Copernicus initiative
and the corresponding development of a family of Sentinel missions. This has
enabled the global monitoring of our planet across the whole electromagnetic
spectrum on an operational and sustained basis.
In addition, a new trend, referred to as “New Space” in the USA or “Space 4.0”
in Europe, is now rapidly emerging through the increasing commoditization and
commercialization of space. In particular, with the rapidly dropping costs of small
sat building, launching and processing, new EO actors including startups and ICT
giants are now entering the space business in masses, forming new constellations
of standardized small sats that deliver a new class of data on our planet with higher
spatial resolution and increased temporal frequency.
These new global data sets from space lead to a far more comprehensive picture
of our planet. This picture is now even more refined via data from billions of smart
and inter-connected sensors referred to as the Internet of Things (IoT).
Such streams of dynamic data on our planet offer new possibilities for scientists
to advance our understanding of how the ocean, atmosphere, land and cryosphere
operate and interact as part on an integrated Earth System. It also represents new
opportunities for entrepreneurs to turn big data into new types of information
services. However, these opportunities come with new challenges for scientists,
businesses, data and software providers who must make sense of the vast and diverse
amount of data by capitalizing on new technologies such as big data analytics.
v
vi Foreword
This book invites you to explore various elements of the big data revolution,
addressing the development of Space 4.0, the new generation of data-driven research
infrastructure (including the emergence of data cubes), new applications integrating
IoT and EO, new business models in the emerging geo-sharing economy, new ways
to support e-learning and digital education, new application of technologies such as
cloud computing, artificial intelligence (AI), and deep learning, and the increasing
role of new actors such as innovative startups, ICT corporates, data scientists and
citizen scientists. By doing so, it aims to stimulate new ideas about how to make the
most of EO and derived information in a rapidly changing environment.
Wishing you an inspiring journey in the exciting field of EO Open Science and
Innovation.
Josef Aschbacher
Director of Earth Observation Programmes
European Space Agency (ESA)
Frascati, Italy
Contents
Part I Join the Geo Revolution

The Changing Landscape of Geospatial Information Markets . . . . . . . . . . . . . 3
Conor O’Sullivan, Nicholas Wise, and Pierre-Philippe Mathieu
The Digital Transformation of Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Ravi Kapur, Val Byfield, Fabio Del Frate, Mark Higgins,
and Sheila Jagannathan
The Open Science Commons for the European Research Area . . . . . . . . . . . . . 43
Tiziana Ferrari, Diego Scardaci, and Sergio Andreozzi
Citizen Science for Observing and Understanding the Earth . . . . . . . . . . . . . . . 69
Mordechai (Muki) Haklay, Suvodeep Mazumdar, and Jessica Wardlaw
Part II Enabling Data Intensive Science

Fostering Cross-Disciplinary Earth Science Through Datacube
Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Peter Baumann, Angelo Pio Rossi, Brennan Bell, Oliver Clements,
Ben Evans, Heike Hoenig, Patrick Hogan, George Kakaletris,
Panagiota Koltsida, Simone Mantovani, Ramiro Marco Figuera,
Vlad Merticariu, Dimitar Misev, Huu Bang Pham, Stephan Siemen,
and Julia Wagemann
Mind the Gap: Big Data vs. Interoperability and Reproducibility
of Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Max Craglia and Stefano Nativi
Cyber-Infrastructure for Data-Intensive Geospatial Computing . . . . . . . . . . . 143
Rajasekar Karthik, Alexandre Sorokine, Dilip R. Patlolla, Cheng Liu,
Shweta M. Gupte, and Budhendra L. Bhaduri
vii
viii Contents
Machine Learning Applications for Earth Observation . . . . . . . . . . . . . . . . . . . . . 165

David J. Lary, Gebreab K. Zewdie, Xun Liu, Daji Wu, Estelle Levetin,
Rebecca J. Allee, Nabin Malakar, Annette Walker, Hamse Mussa, Antonio
Mannino, and Dirk Aurin
New Generation Platforms for Exploration of Crowdsourced
Geo-Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Maria Antonia Brovelli, Marco Minghini, and Giorgio Zamboni
Part III Use Cases Open Science and Innovation

Mapping Land Use Dynamics Using the Collective Power
of the Crowd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Christoph Aubrecht, Joachim Ungar, Dilek Ozceylan Aubrecht,
Sérgio Freire, and Klaus Steinnocher
The Emergence of the GeoSharing Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Ursula Benz and Manfred Krischke
Sustainable Agriculture and Smart Farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
Heike Bach and Wolfram Mauser
Earth Observation Data for Enterprise Business Applications . . . . . . . . . . . . . 271
Hinnerk Gildhoff
Development of an Earth Observation Cloud Platform in Support
to Water Resources Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275
Andreea Bucur, Wolfgang Wagner, Stefano Elefante, Vahid Naeimi,
and Christian Briese
Putting Big Data Innovation into Action for Development . . . . . . . . . . . . . . . . . . 285
Trevor Monroe, Stephanie Debere, Kwawu Mensa Gaba, David Newhouse,
and Talip Killic
Mapping Floods and Assessing Flood Vulnerability for Disaster
Decision-Making: A Case Study Remote Sensing Application
in Senegal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
Bessie Schwarz, Gabriel Pestre, Beth Tellman, Jonathan Sullivan,
Catherine Kuhn, Richa Mahtta, Bhartendu Pandey, and Laura Hammett
Earth Observation and Geospatial Implementation: Fueling
Innovation in a Changing World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
Sudhir Raj Shrestha, Matthew Tisdale, Steve Kopp, and Brett Rose
Artificial Intelligence and Earth Observation to Explore Water
Quality in the Wadden Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311
Luigi Ceccaroni, Filip Velickovski, Meinte Blaas, Marcel R. Wernand,
Anouk Blauw, and Laia Subirats
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
Part I
Join the Geo Revolution
The Changing Landscape of Geospatial
Information Markets
Conor O’Sullivan, Nicholas Wise, and Pierre-Philippe Mathieu
Abstract We live in an increasingly global, connected and digital world. In less

than a decade or so, fast developments in digital technologies, such as the Cloud,
Internet, wireless network, and most importantly mobile telephony, have dramati-
cally changed the way we work, live and play. Rapid advances in Information and
Communication Technologies (ICT) foster a new world of cross-disciplinary data-
intensive research characterised by openness, transparency, access to large volume
of complex data, availability of community open tools, unprecedented level of
computing power, and new collaboration among researchers and new actors such
as citizen scientists. Identifying and understanding the key drivers of change in the
data economy and EO sector (including technological, human, cultural and legal
factors) is essential to providing context on which to build an EO strategy for the
twenty-first century. The emergence of cloud computing is already transforming
the way we access and exploit data. This has led to a paradigm shift in the way
to distribute and process data, and in creating platforms that drive innovation and
growth in user applications.
Introduction
The Earth Observation industry, part of the wider data economy, is experiencing a
number of factors that are driving change across the value chain. These include,
to name a few, leveraging IT infrastructure such as cloud computing, the rise
of platforms and the Internet of Things (IoT), interconnected terrestrial and
space-borne systems, diversification of business models and open data policies.
Copernicus, the European flagship programme to provide geo-information services
to EU policy makers, provides a strong opportunity as market driver for EO-based
services. According to a recent survey by the European Association of Remote
C. O’Sullivan () • N. Wise

Satellite Applications Catapult, Harwell, UK
e-mail: Conor.OSullivan@sa.catapult.org.uk; nicholas.wise@sa.catapult.org.uk
P.-P. Mathieu
ESA/ESRIN, Frascati, Italy
e-mail: Pierre.philippe.mathieu@esa.int
© The Author(s) 2018 3

P.-P. Mathieu, C. Aubrecht (eds.), Earth Observation Open Science and Innovation,
ISSI Scientific Report Series 15, https://doi.org/10.1007/978-3-319-65633-5_1
4 C. O’Sullivan et al.
Sensing Companies (EARSC), Industry is optimistic about the positive impact the
Copernicus programme will have on their business (EARSC 2015).
The European Commission’s Digital Single Market Package is a genuine driver
for EU growth and new jobs. It highlights the benefits of a stronger Digital Single
Market and its potential for higher growth and new jobs, and increasing global
competitiveness:
Full and efficient exploitation of tools and services such as Cloud Computing, Big Data,
Automation, Internet of Things and Open Data can drive for better productivity and better
services, and therefore should be facilitated, including through market driven solutions,
R&D and the promotion of the necessary skills and capacity building, along with further
ICT standardisation and interoperability (Council of the European Union 2015)
The volume, variety and velocity of data are increasingly rapidly and “Big
Data” acts as the oil in the supply chain for many industries. Within the next few
years, ESA spacecraft alone will obtain approximately 25 PB of Earth Observation
(EO) data as a result of the Copernicus programme (Di Meglio et al. 2014). In
addition, data is generated from a multitude of sources, including small satellite
constellations, ground and airborne sensors (e.g. Unmanned Aerial Vehicles, UAVs),
social media, machine to machine (M2M) communications and crowdsourcing.
The cost-effective 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, 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 most recent round of funding 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-ups (Financial Times 2015).
Data value chain. Source: Digital Catapult (2014)

The Changing Landscape of Geospatial Information Markets 5
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
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. Commercial cloud
providers, like Microsoft Azure and Amazon Web Services, 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 A C140 billion worth of value a year (The Economist 2013). 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. Source: Satellite Applications Catapult
The key drivers of change in the data economy impacting the EO market
include:
• Rise of the platforms: leveraging cloud computing infrastructure to process
more and more layers of data, from multiple sources. Simplifying applications
development and building an app ecosystem around scalable, on-demand IT
infrastructure.
• Data as a Service: user manages the application, everything else is delivered as
a service. Moving users closer to the data (“data gravity”) via Content Delivery
Networks (CDNs).1
• Open data policies: demand from users and government policies changing
towards improved access to data and tools.
• New business models: growing an ecosystem of researchers and developers so
that people can easily gain access to and use a multitude of data analysis services
quickly, through cloud and high performance computing (HPC) platforms, to add
knowledge and open source tools for others’ benefit.
• Sensor use growing: Internet of Things and sensors intelligently working at the
edge of networks, complementarity of space-borne and terrestrial data.
• Crowdsourcing: citizen science platforms and their commercial capability.
• Disruptive innovation: introduces a new value proposition. They either create
new markets or reshape existing ones.
Rise of the Platforms
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 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,
1
CDNs: a content delivery network or content distribution network (CDN) is a large distributed
system of servers deployed in multiple data centres across the Internet. The goal of a CDN
is to serve content to end-users with high availability and high performance. Examples include
Microsoft Azure and Amazon CloudFront.
Each new computing cycle typically generates around 10 the installed base of the previous
cycle. Source: Kleiner Perkins Caufield Buyers (2014) Internet Trends 2014, see www.kpcb.com/
InternetTrends retrieved on December 5th 2014
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) (Woodside Capital Partners 2014).
Cloud computing services are typically segmented into three different areas:
1. Infrastructure as a Service (IaaS)—third-party provider hosts virtualised com-
puting resources over the Internet, through which customers can host and develop
services and applications.
2. 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.
3. Software as a Service (SaaS)—software is hosted in the cloud, but appears on
devices with full functionality.
Access to the Cloud and platforms are required to capitalise on the data and
tools being created to make it easier and faster to discover, process and action
on EO datasets. The cloud provides scalability and flexibility in a cost-efficient
manner. There should be a combination of ESA and cloud providers supporting
the communities within the EO ecosystem to develop tools and ensure data access.
Commercial cloud, capitalising on co-location of computing resources and data
storage, is now becoming widely adopted as it offers several advantages enabling
users to (1) perform data-intensive science, (2) ensure traceability of workflows,
Cloud delivery options. Source: 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.
input data sets and therefore enabling to reproduce results, (3) facilitate integration
with other non-EO data through standard web services (e.g. open data and IoT
for smart cities) and (4) open new business models, whereby commercial data,
proprietary software, apps and computing resources can now be rented on-demand
(as opposed to purchased) to generate Information as a Service. Given the number of
EO platforms that are being developed and coming on-line, the challenge revolves
around the technical and economic aspects of interoperability, such as:
• A common EO data pool from all EO missions in Europe;
• A processing capability management (sharing of resources and cloud services for
processing);
• Sustainability through fair and democratic access to all resources (data, intel-
lectual properties, enabling technologies/computing) by means of underlying
implementation principles based on brokerage within an open environment;
• Federated user interfaces subsystems (e.g. interlinked EO data catalogues),
interface and standards definition and agreement;
• Development of common value-creation techniques (research in data analytics
and information retrieval, information visualization, data mining, fusion of in
situ data with geo-information, etc.);
• Definition and application of commonly agreed data management principles

to ensure data discoverability, accessibility, usability, preservation and
curation.
• With the emergence of cloud-based storage and computing, users are now able
to easily and cheaply process data on demand. This leads to a paradigm shift
whereby data providers are now moving the computations—and therefore the
users—to the data, rather than the data to the users. In this context, ESA is
developing a series of EO Thematic application platforms or Virtual Research
Environments (e.g. Geohazard SuperSites) to exploit the data for different
applications and communities.
• Increasing data access and data liquidity, through interaction with the wider data
economy and embracing ICT trends (Cloud, open software, APIs), will support
new products with market opportunity. This will pave the way for new and
existing organisations that have valuable skills or market knowledge, to enter the
EO community by making it easier to discover, access and process EO datasets.
Both the “democratisation” of GIS skills and making EO tools and interfaces
more user friendly are crucial to enabling a much wider range of users.
Core to the data access agenda is cloud computing, which allows users to use on-
demand computing resources co-located with data staged for analysis in the cloud.
Commercial cloud providers, such as Amazon Web Services and Microsoft Azure,
offer various tools and techniques to enable the build, deployment and management
of scalable applications, with the aim of reaching a global audience. Through these
platforms, underlying computer and storage resources scale automatically to match
application demand. When data is made publicly available via a commercial cloud
provider, users can access and analyse the data extremely rapidly and at very large
scale.
Core also to increasing data access and liquidity are powerful functional inter-
faces (APIs) that allow discovery and retrieval of EO data and products, thereby
delivering analysis ready data. The cloud based API Economy is key to accelerating
value, improving performance, and extending products and services to the widest
possible audience. User friendly API’s are essential—they must be flexible enabling
enhanced features so entrepreneurs and developers can use combinations of data
for specific themes. A well-established way to simplify access to services and data
is to implement an API since this enables a developer to exploit their specialist
knowledge to create higher level products and services without having to invest a
large amount of time and effort to access the relevant data and create, and then
verify, basic services. Companies like Planet Spire are following this approach by
developing APIs and allowing “plug-and-play” access to them. The biggest player
in the satellite data industry, DigitalGlobe has recently announced the launch of
Maps API to allow software developers to embed satellite images, maps and other
geospatial content into their mobile and web applications.
Data as a Service
More recently, the concept of data as a service (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 users.2 Like all members of the “as a Service” family, DaaS is based on the
concept that the product, 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.
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 2020.3 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 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 mash-ups (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.
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
2
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.
3
IBM (2013) Global Technology Outlook.
APIs are enabling more and more devices to connect. Source: IBM (2014) Exposing and Managing
Enterprise Services with IBM API Management.
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).
Open Data Policies
Data has been referred to as the new raw material of the twenty-first 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 world; entrepreneurs pounced on this opportunity.
Car navigation, precision farming and three million American jobs now depend
on GPS.4 Official weather data are also public and avidly used by everyone from
insurers to ice-cream sellers. All data created or collected by America’s federal
government must now be made available free to the public, unless this would violate
privacy, confidentiality or security.
Open and machine-readable is the new default for government information. (US Presi-
dent, Barack Obama (2013))5
Many countries have moved in the same direction. In Europe, the information held by
governments could be used to generate an estimated A C140 billion a year.6 McKinsey
estimates the potential annual value to Europe’s public sector administration at A
C250
billion.7
The emerging open data ‘Marketplace’. Source: Deloitte (2012) Open growth: Stimulating
demand for open data in the UK, see www2.deloitte.com/uk/en/pages/deloitte-analytics/articles/
stimulating-demand-for-open-data-in-the-uk.html retrieved on 8th February 2015.
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).
4
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
Ibid.
6
Ibid.
7
McKinsey Global Institute (2011) Big data: The next frontier for innovation, competition, and
productivity.
In The Cathedral & the Bazaar, a book by Eric 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 services.8
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 3000 new ones now appearing every week.9
Android, with a global smartphone operating system market share of around 80%,10
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.11
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 A C8 billion in revenue for developers in 2014 and to date, App
8
Lathrup, D. and Ruma, L. (2010) Open Government: Collaboration, Transparency, and Partici-
pation in Practice, O’Reilly Media.
9
Ibid.
10
Business Insider website, see www.businessinsider.com/iphone-v-android-market-share-2014-
5?IR=T retrieved on 3rd March 2015.
11
Dave McCrory (2011), Gathering Moss, Data Gravity, and Context, see www.datagravity.org
retrieved on 17th March 2015.
Store developers have earned a cumulative A

C20 billion from the sale of apps and
games.12 According to Flurry Analytics, a mobile analytics firm, in 2014 overall
app usage grew by 76%.
New Business Models
New entrants to the EO sector, including Planet 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 has announced that it will release a
developer API this year.13
Business models are also emerging to develop a more integrated network of
stakeholders. CloudEO, a German company that supplies EO data on a pay-per-
use or subscription basis,14 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 this.15
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 monitoring.16
Canadian company, UrtheCast, has been granted the exclusive right to operate two
cameras on the Russian module of the International Space Station (ISS).17 As
the ISS passes over the Earth, UrtheCast’s twin cameras capture and download
large amounts of HD (5 m resolution) video and photos. This data is then stored
12
Apple (2015) see www.apple.com/pr/library/2015/01/08App-Store-Rings-in-2015-with-New-
Records.html retrieved on 19th January 2015.
13
Planet Labs website, see www.planet.com/flock1/ retrieved on 29th January 2015.
14
Henry, C. (2014) CloudEO Starts ‘Virtual Constellation’ Access with Beta Online Mar-
ketplace, see www.satellitetoday.com/technology/2014/03/26/cloudeo-starts-virtual-constellation-
access-with-beta-online-marketplace/ retrieved on 28th January 2015.
15
M. Kazmierski et al (2014) GEOSUD SDI: accessing Earth Observation data collec-
tions with semantic-based services, see www.agile-online.org/Conference_Paper/cds/agile_2014/
agile2014_138.pdf retrieved on 19th January 2015.
16
Northern Sky Research (2013) Satellite-Based Earth Observation, 5th Edition.
17
UrtheCast (2013), see www.investors.urthecast.com/interactive/lookandfeel/4388192/
UrtheCast-Investor-Deck.pdf retrieved on 29th January 2015.
and made available via APIs on the basis of a pay-for-use model.18 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 advertising-like revenue from companies that will have
their logos featured on the video in relation to their locations.19
Sensor Use Growing
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
insights.20
The IoT is forecast to reach 26 billion installed units by 2020, up from 900
million 5 years ago.21 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.
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 is not 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
18
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.
19
UstreamTV (2012) UrtheCast Business Model, see www.ustream.tv/recorded/26973814
retrieved on 19th January 2015.
20
Cisco (2011) The Internet of Things: How the Next Evolution of the Internet Is Changing
Everything.
21
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.
Rising proliferation of devices. Source: Kleiner Perkins Caufield Buyers (2014), Internet Trends
2014, see www.kpcb.com/InternetTrends retrieved on December 5th 2014.
products are kept at throughout the supply chain can be tracked, food companies
have a better chance of extending shelf-life and reducing waste.22
Toyota has announced the development, in Japan, of the “Big Data Traffic
Information Service”, 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 measures.23
Crowdsourcing
There has been an explosion of activity in the area termed citizen science, crowd-
sourcing and volunteered geographic information (VGI). EO data is contributing to
problem solving on a global scale. Some of the highest profile successes happen
when this data is used in citizen science projects, where the power of large numbers
of humans getting involved can achieve results that are simply not possible with
computers alone:
22
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.
23
ABI/INFORM Global (2013) JAPAN: Toyota to launch Big Data Traffic Information Service.
• Speedy provision of information to rescue and support services following natural

or manmade disasters;
• Identifying evidence of illegal activities, including poaching and people traffick-
ing;
• Improving maps and monitoring the environment.
Citizen science and crowdsourcing are being used in a very diverse range of
applications, including archaeology, medicine, mapping, astronomy and disaster
response. Science and mapping-related projects may either be organised through
one of the aggregator platforms, such as Tomnod and Zooniverse, or stem from
calls from organisations such as the UN and scientific bodies.
The Geo-Wiki project, for example, engages people in visual validation of maps,
with a focus on land cover and land use. The tool can bring together datasets to
be viewed on top of high resolution satellite imagery and has already been used to
challenge assumptions about land available for biofuels. The tool also serves as a
visualisation platform, bringing together global land cover datasets in a single place,
which can be viewed on top of very high resolution satellite imagery from Google
Earth and Bing.
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 engagement with science.24 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 one 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.
24
The Royal Society (2012) Science as an Open Enterprise, The Royal Society Science Policy
Centre report.
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 Zoo.25
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 innovations.26
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.
25
Wikipedia, see www.en.wikipedia.org/wiki/Zooniverse_%28citizen_science_project%29
retrieved on 18th January 2015.
26
Christensen et al (2004) Seeing what’s next: using the theories of innovation to predict industry
change, Harvard Business School Press.
Cloud Computing
Cloud-delivered enterprise solutions fit Christensen’s concept of disruptive innova-

tion. 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, they’re spreading
throughout organisations and solving some of the most demanding problems.27
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 cannot 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.
Forty percent of Azure’s revenue comes from start-ups and independent software
vendors (ISVs), and 50% of Fortune 500 companies use Windows Azure. Microsoft
27
Fortune (2011) see www.fortune.com/2011/09/27/is-the-cloud-the-ultimate-disruptive-
innovation/ retrieved on 20th January 2015.
has invested $15 billion to build its cloud infrastructure, comprised of a large global
portfolio of more than 100 datacentres, one million servers, content distribution
networks, edge computing nodes, and fibre-optic networks.28
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 all. (Dr. Jeff Norris (2010),
NASA Jet Propulsion Laboratory)29
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 cloud-based 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.
28
Microsoft website see www.news.microsoft.com/cloud/index.html retrieved on 3rd March 2015.
29
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.
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 developers”.30 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
remote-sensing 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’ benefit.31 (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 data.32 (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.
30
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.
31
Ibid.
32
Ibid.
Another random document with
no related content on Scribd:
The Project Gutenberg eBook of Answer, please
answer
This ebook is for the use of anyone anywhere in the United States
and most other parts of the world at no cost and with almost no
restrictions whatsoever. You may copy it, give it away or re-use it
under the terms of the Project Gutenberg License included with this
ebook or online at www.gutenberg.org. If you are not located in the
United States, you will have to check the laws of the country where
you are located before using this eBook.
Title: Answer, please answer
Author: Ben Bova
Illustrator: George Schelling
Release date: November 28, 2023 [eBook #72247]
Language: English
Original publication: New York, NY: Ziff-Davis Publishing Company,

1962
Credits: Greg Weeks, Mary Meehan and the Online Distributed

Proofreading Team at http://www.pgdp.net
*** START OF THE PROJECT GUTENBERG EBOOK ANSWER,

PLEASE ANSWER ***
Answer, Please Answer
By BEN BOVA
Illustrated by SCHELLING
Astronomer Bova draws upon the facts of his field to

weave a story that will grip your emotions and tantalize
your mind—long after you have finished reading it.
[Transcriber's Note: This etext was produced from

Amazing Stories October 1962.
Extensive research did not uncover any evidence that
the U.S. copyright on this publication was renewed.]
We had been at the South Pole a week. The outside thermometer
read fifty degrees below zero, Fahrenheit. The winter was just
beginning.
"What do you think we should transmit to McMurdo?" I asked Rizzo.
He put down his magazine and half-sat up in his bunk. For a moment
there was silence, except for the nearly inaudible hum of the
machinery that jammed our tiny dome, and the muffled shrieking of
the ever-present wind, above us.
Rizzo looked at the semi-circle of control consoles, computers, and
meteorological sensors with an expression of disgust that could be
produced only by a drafted soldier.
"Tell 'em it's cold, it's gonna get colder, and we've both got
appendicitis and need replacements immediately."
"Very clever," I said, and started touching the buttons that would
automatically transmit the sensors' memory tapes.
Rizzo sagged back into his bunk. "Why?" He asked the curved
ceiling of our cramped quarters. "Why me? Why here? What did I
ever do to deserve spending the whole goddammed winter at the
goddammed South Pole?"
"It's strictly impersonal," I assured him. "Some bright young
meteorologist back in Washington has convinced the Pentagon that
the South Pole is the key to the world's weather patterns. So here
we are."
"It doesn't make sense," Rizzo continued, unhearing. His dark,
broad-boned face was a picture of wronged humanity. "Everybody
knows that when the missiles start flying, they'll be coming over the
North Pole.... The goddammed Army is a hundred and eighty
degrees off base."
"That's about normal for the Army, isn't it?" I was a drafted soldier,
too.
Rizzo swung out of the bunk and paced across the dimly-lit room. It
only took a half-dozen paces; the dome was small and most of it was
devoted to machinery.
"Don't start acting like a caged lion," I warned. "It's going to be a long
winter."
"Yeah, guess so." He sat down next to me at the radio console and
pulled a pack of cigarets from his shirt pocket. He offered one to me,
and we both smoked in silence for a minute or two.
"Got anything to read?"
I grinned. "Some microspool catalogues of stars."
"Stars?"
"I'm an astronomer ... at least, I was an astronomer, before the
National Emergency was proclaimed."
Rizzo looked puzzled. "But I never heard of you."
"Why should you?"
"I'm an astronomer too."
"I thought you were an electronicist."
He pumped his head up and down. "Yeah ... at the radio astronomy
observatory at Greenbelt. Project OZMA. Where do you work?"
"Lick Observatory ... with the 120-inch reflector."
"Oh ... an optical astronomer."
"Certainly."
"You're the first optical man I've met." He looked at me a trifle
queerly.
I shrugged. "Well, we've been around a few millennia longer than
you static-scanners."
"Yeah, guess so."
"I didn't realize that Project OZMA was still going on. Have you had
any results yet?"
It was Rizzo's turn to shrug. "Nothing yet. The project has been
shelved for the duration of the emergency, of course. If there's no
war, and the dish doesn't get bombed out, we'll try again."
"Still listening to the same two stars?"
"Yeah ... Tau Ceti and Epsilon Eridani. They're the only two Sun-type
stars within reasonable range that might have planets like Earth."
"And you expect to pick up radio signals from an intelligent race."
"Hope to."
I flicked the ash off my cigaret. "You know, it always struck me as
rather hopeless ... trying to find radio signals from intelligent
creatures."
"Whattaya mean, hopeless?"
"Why should an intelligent race send radio signals out into interstellar
space?" I asked. "Think of the power it requires, and the likelihood
that it's all wasted effort, because there's no one within range to talk
to."
"Well ... it's worth a try, isn't it ... if you think there could be intelligent
creatures somewhere else ... on a planet of another star."
"Hmph. We're trying to find another intelligent race; are we
transmitting radio signals?"
"No," he admitted. "Congress wouldn't vote the money for a
transmitter that big."
"Exactly," I said. "We're listening, but not transmitting."
Rizzo wasn't discouraged. "Listen, the chances—just on statistical
figuring alone—the chances are that there're millions of other solar
systems with intelligent life. We've got to try contacting them! They
might have knowledge that we don't have ... answers to questions
that we can't solve yet...."
"I completely agree," I said. "But listening for radio signals is the
wrong way to do it."
"Huh?"
"Radio broadcasting requires too much power to cover interstellar
distances efficiently. We should be looking for signals, not listening
for them."
"Looking?"
"Lasers," I said, pointing to the low-key lights over the consoles.
"Optical lasers. Super-lamps shining out in the darkness of the void.
Pump in a modest amount of electrical power, excite a few trillion
atoms, and out comes a coherent, pencil-thin beam of light that can
be seen for millions of miles."
"Millions of miles aren't lightyears," Rizzo muttered.
"We're rapidly approaching the point where we'll have lasers capable
of lightyear ranges. I'm sure that some intelligent race somewhere in
this galaxy has achieved the necessary technology to signal from
star to star—by light beams."
"Then how come we haven't seen any?" Rizzo demanded.
"Perhaps we already have."
"What?"
"We've observed all sorts of variable stars—Cepheids, RR Lyrae's, T
Tauri's. We assume that what we see are stars, pulsating and
changing brightness for reasons that are natural, but unexplainable
to us. Now, suppose what we are really viewing are laser beams,
signalling from planets that circle stars too faint to be seen from
Earth?"
In spite of himself, Rizzo looked intrigued.
"It would be fairly simple to examine the spectra of such light
sources and determine whether they're natural stars or artificial laser
beams."
"Have you tried it?"
I nodded.
"And?"
I hesitated long enough to make him hold his breath, waiting for my
answer. "No soap. Every variable star I've examined is a real star."
He let out his breath in a long, disgusted puff. "Ahhh, you were
kidding all along. I thought so."
"Yes," I said. "I suppose I was."
Time dragged along in the weather dome. I had managed to
smuggle a small portable telescope along with me, and tried to make
observations whenever possible. But the weather was usually too
poor. Rizzo, almost in desperation for something to do, started to
build an electronic image-amplifier for me.
Our one link with the rest of the world was our weekly radio message
from McMurdo. The times for the messages were randomly
scrambled, so that the chances of their being intercepted or jammed
were lessened. And we were ordered to maintain strict radio silence.
As the weeks sloughed on, we learned that one of our manned
satellites had been boarded by the Reds at gunpoint. Our space-
crews had put two Red automated spy-satellites out of commission.
Shots had been exchanged on an ice-island in the Arctic. And six
different nations were testing nuclear bombs.
We didn't get any mail of course. Our letters would be waiting for us
at McMurdo when we were relieved. I thought about Gloria and our
two children quite a bit, and tried not to think about the blast and
fallout patterns in the San Francisco area, where they were.
"My wife hounded me until I spent pretty nearly every damned cent I
had on a shelter, under the house," Rizzo told me. "Damned shelter
is fancier than the house. She's the social leader of the disaster set.
If we don't have a war, she's gonna feel damned silly."
I said nothing.
The weather cleared and steadied for a while (days and nights were
indistinguishable during the long Antarctic winter) and I split my time
evenly between monitoring the meteorological sensors and
observing the stars. The snow had covered the dome completely, of
course, but our "snorkel" burrowed through it and out into the air.
"This dome's just like a submarine, only we're submerged in snow
instead of water," Rizzo observed. "I just hope we don't sink to the
bottom."
"The calculations show that we'll be all right."
He made a sour face. "Calculations proved that airplanes would
never get off the ground."
The storms closed in again, but by the time they cleared once more,
Rizzo had completed the image-amplifier for me. Now, with the tiny
telescope I had, I could see almost as far as a professional
instrument would allow. I could even lie comfortably in my bunk,
watch the amplifier's viewscreen, and control the entire set-up
remotely.
Then it happened.
At first it was simply a curiosity. An oddity.
I happened to be studying a Cepheid variable star—one of the huge,

very bright stars that pulsate so regularly that you can set your watch
by them. It had attracted my attention because it seemed to be
unusually close for a Cepheid—only 700 lightyears away. The
distance could be easily gauged by timing the star's pulsations.[1]
I talked Rizzo into helping me set up a spectrometer. We scavenged
shamelessly from the dome's spare parts bin and finally produced an
instrument that would break up the light of the star into its
component wavelengths, and thereby tell us much about the star's
chemical composition and surface temperature.
At first I didn't believe what I saw.
The star's spectrum—a broad rainbow of colors—was criss-crossed
with narrow dark lines. That was all right. They're called absorption
lines; the Sun has thousands of them in its spectrum. But one line—
one—was an insolently bright emission line. All the laws of physics
and chemistry said it couldn't be there.
But it was.
We photographed the star dozens of times. We checked our
instruments ceaselessly. I spent hours scanning the star's "official"
spectrum in the microspool reader. The bright emission line was not
on the catalogue spectrum. There was nothing wrong with our
instruments.
Yet the bright line showed up. It was real.
"I don't understand it," I admitted. "I've seen stars with bright
emission spectra before, but a single bright line in an absorption
spectrum! It's unheard-of. One single wavelength ... one particular
type of atom at one precise energy-level ... why? Why is it emitting
energy when the other wavelengths aren't?"
Rizzo was sitting on his bunk, puffing a cigaret. He blew a cloud of
smoke at the low ceiling. "Maybe it's one of those laser signals you
were telling me about a couple weeks ago."
I scowled at him. "Come on, now. I'm serious. This thing has me
puzzled."
"Now wait a minute ... you're the one who said radio astronomers
were straining their ears for nothing. You're the one who said we
ought to be looking. So look!" He was enjoying his revenge.
I shook my head, and turned back to the meteorological equipment.
But Rizzo wouldn't let up. "Suppose there's an intelligent race living
on a planet near a Cepheid variable star. They figure that any other
intelligent creatures would have astronomers who'd be curious about
their star, right? So they send out a laser signal that matches the
star's pulsations. When you look at the star, you see their signal.
What's more logical?"
"All right," I groused. "You've had your joke...."
"Tell you what," he insisted. "Let's put that one wavelength into an
oscilloscope and see if a definite signal comes out. Maybe it'll spell
out 'Take me to your leader' or something."
I ignored him and turned my attention to Army business. The

meteorological equipment was functioning perfectly, but our orders
read that one of us had to check it every twelve hours. So I checked
and tried to keep my eyes from wandering as Rizzo tinkered with a
photocell and oscilloscope.
"There we are," he said, at length. "Now let's see what they're telling
us."
In spite of myself I looked up at the face of the oscilloscope. A
steady, gradually sloping greenish line was traced across the screen.
"No message," I said.
Rizzo shrugged elaborately.
"If you leave the 'scope on for two days, you'll find that the line
makes a full swing from peak to null," I informed him. "The star
pulsates every two days, bright to dim."
"Let's turn up the gain," he said, and he flicked a few knobs on the
front of the 'scope.
The line didn't change at all.
"What's the sweep speed?" I asked.
"One nanosecond per centimeter." That meant that each centimeter-
wide square on the screen's face represented one billionth of a
second. There are as many nanoseconds in one second as there are
seconds in thirty-two years.
"Well, if you don't get a signal at that sensitivity, there just isn't any
signal there," I said.
Rizzo nodded. He seemed slightly disappointed that his joke was at
an end. I turned back to the meteorological instruments, but I
couldn't concentrate on them. Somehow I felt disappointed, too.
Subconsciously, I suppose, I had been hoping that Rizzo actually
would detect a signal from the star. Fool! I told myself. But what
could explain that bright emission line? I glanced up at the
oscilloscope again.
And suddenly the smooth steady line broke into a jagged series of
millions of peaks and nulls!
I stared at it.
Rizzo was back on his bunk again, reading one of his magazines. I
tried to call him, but the words froze in my throat. Without taking my
eyes from the flickering 'scope, I reached out and touched his arm.
He looked up.
"Holy Mother of God," Rizzo whispered.
For a long time we stared silently at the fluttering line dancing across
the oscilloscope screen, bathing our tiny dome in its weird greenish
light. It was eerily fascinating, hypnotic. The line never stood still; it
jabbered and stuttered, a series of millions of little peaks and nulls,
changing almost too fast for the eye to follow, up and down, calling to
us, speaking to us, up, down, never still, never quiet, constantly
flickering its unknown message to us.
The line never stood still; millions of little peaks and nulls
calling to us, speaking to us, never still, never quiet, constantly
flickering its unknown message to us.
"Can it be ... people?" Rizzo wondered. His face, bathed in the

greenish light, was suddenly furrowed, withered, ancient: a mixture
of disbelief and fear.
"What else could it be?" I heard my own voice answer. "There's no
other explanation possible."
We sat mutely for God knows how long.
Finally Rizzo asked, "What do we do now?"
The question broke our entranced mood. What do we do? What
action do we take? We're thinking men, and we've been contacted
by other creatures that can think, reason, send a signal across seven
hundred lightyears of space. So don't just sit there in stupified awe.
Use your brain, prove that you're worthy of the tag sapiens.
"We decode the message," I announced. Then, as an after-thought,
"But don't ask me how."
We should have called McMurdo, or Washington. Or perhaps we
should have attempted to get a message through to the United
Nations. But we never even thought of it. This was our problem.
Perhaps it was the sheer isolation of our dome that kept us from
thinking about the rest of the world. Perhaps it was sheer luck.
"If they're using lasers," Rizzo reasoned, "they must have a
technology something like ours."
"Must have had," I corrected. "That message is seven hundred years
old, remember. They were playing with lasers when King John was
signing the Magna Charta and Genghis Khan owned most of Asia.
Lord knows what they have now."
Rizzo blanched and reached for another cigaret.
I turned back to the oscilloscope. The signal was still flashing across
its face.
"They're sending out a signal," I mused, "probably at random. Just
beaming it out into space, hoping that someone, somewhere will pick
it up. It must be in some form of code ... but a code that they feel can
be easily cracked by anyone with enough intelligence to realize that
there's a message there."
"Sort of an interstellar Morse code."
I shook my head. "Morse code depends on both sides knowing the
code. There's no key."
"Cryptographers crack codes."
"Sure. If they know what language is being used. We don't know the
language, we don't know the alphabet, the thought processes ...
nothing."
"But it's a code that can be cracked easily," Rizzo muttered.
"Yes," I agreed. "Now what the hell kind of a code can they assume
will be known to another race that they've never seen?"
Rizzo leaned back on his bunk and his face was lost in shadows.
"An interstellar code," I rambled on. "Some form of presenting
information that would be known to almost any race intelligent
enough to understand lasers...."
"Binary!" Rizzo snapped, sitting up on the bunk.
"What?"
"Binary code. To send a signal like this, they've gotta be able to write
a message in units that're only a billionth of a second long. That
takes computers. Right? Well, if they have computers, they must
figure that we have computers. Digital computers run on binary code.
Off or on ... go or no-go. It's simple. I'll bet we can slap that signal on
a tape and run it through our computer here."
"To assume that they use computers exactly like ours...."
"Maybe the computers are completely different," Rizzo said excitedly,
"but the binary code is basic to them all. I'll bet on that! And this
computer we've got here—this transistorized baby—she can handle
more information than the whole Army could feed into her. I'll bet
nothing has been developed anywhere that's better for handling
simple one-plus-one types of operations."
I shrugged. "All right. It's worth a trial."
It took Rizzo a few hours to get everything properly set up. I did
some arithmetic while he worked. If the message was in binary code,
that meant that every cycle of the signal—every flick of the dancing
line on our screen—carried a bit of information. The signal's
wavelength was 5000 Angstroms; there are a hundred million
Angstrom units to the centimeter; figuring the speed of light ... the
signal could carry, in theory at least, something like 600 trillion bits of
information per second.
I told Rizzo.
"Yeah, I know. I've been going over the same numbers in my head."
He set a few switches on the computer control board. "Now let's see
how many of the 600 trillion we can pick up." He sat down before the
board and pressed a series of buttons.
We watched, hardly breathing, as the computer's spools began
spinning and the indicator lights flashed across the control board.
Within a few minutes, the printer chugged to life.
Rizzo swivelled his chair over to the printer and held up the unrolling
sheet in a trembling hand.
Numbers. Six-digit numbers. Completely meaningless.
"Gibberish," Rizzo snapped.
It was peculiar. I felt relieved and disappointed at the same time.
"Something's screwy," Rizzo said. "Maybe I fouled up the circuits...."
"I don't think so," I answered. "After all, what did you expect out of
the computer? Shakespearean poetry?"
"No, but I expected numbers that would make some sense. One and
one, maybe. Something that means something. This stuff is
nowhere."
Our nerves must have really been wound tight, because before we
knew it we were in the middle of a nasty argument—and it was over
nothing, really. But in the middle of it:
"Hey, look," Rizzo shouted, pointing to the oscilloscope.
The message had stopped. The 'scope showed only the calm,
steady line of the star's basic two-day-long pulsation.
It suddenly occurred to us that we hadn't slept for more than 36
hours, and we were both exhausted. We forgot the senseless
argument. The message was ended. Perhaps there would be
another; perhaps not. We had the telescope, spectrometer,
photocell, oscilloscope, and computer set to record automatically.
We collapsed into our bunks. I suppose I should have had
monumental dreams. I didn't. I slept like a dead man.
When we woke up, the oscilloscope trace was still quiet.

"Y'know," Rizzo muttered, "it might just be a fluke ... I mean, maybe
the signals don't mean a damned thing. The computer is probably
translating nonsense into numbers just because it's built to print out
numbers and nothing else."
"Not likely," I said. "There are too many coincidences to be
explained. We're receiving a message, I'm certain of it. Now we've
got to crack the code."
As if to reinforce my words, the oscilloscope trace suddenly erupted
into the same flickering pattern. The message was being sent again.
We went through two weeks of it. The message would run through
for seven hours, then stop for seven. We transcribed it on tape 48
times and ran it through the computer constantly. Always the same
result—six-digit numbers; millions of them. There were six different
seven-hour-long messages, being repeated one after the other,
constantly.
We forgot the meteorological equipment. We ignored the weekly
messages from McMurdo. The rest of the world became a
meaningless fiction to us. There was nothing but the confounded,
tantalizing, infuriating, enthralling message. The National
Emergency, the bomb tests, families, duties—all transcended, all
forgotten. We ate when we thought of it and slept when we couldn't
keep our eyes open any longer. The message. What was it? What
was the key to unlock its meaning?

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Sensitivity Analysis in Earth Observation Modelling 1st

Understanding Earth Observation The Electromagnetic

Knowledge Discovery in Big Data from Astronomy and

Geodetic Time Series Analysis in Earth Sciences Jean-

Dynamics Games and Science International Conference and

Open Innovation Business Modeling João Leitão

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The world of Earth Observation (EO) is rapidly changing as a result of exponential

Part I Join the Geo Revolution

Part II Enabling Data Intensive Science

Machine Learning Applications for Earth Observation . . . . . . . . . . . . . . . . . . . . . 165

Part III Use Cases Open Science and Innovation

Conor O’Sullivan, Nicholas Wise, and Pierre-Philippe Mathieu

Abstract We live in an increasingly global, connected and digital world. In less

C. O’Sullivan () • N. Wise

© The Author(s) 2018 3

Data value chain. Source: Digital Catapult (2014)

Rise of the Platforms

Cloud computing refers to accessing highly scalable computing resources through

• Definition and application of commonly agreed data management principles

Open Data Policies

Store developers have earned a cumulative A

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Sensor Use Growing

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Defined by Clayton Christensen, disruptive innovation points to situations in which

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Business Models in Cloud Computing

Microsoft Azure delivers general purpose platform as a service (PaaS), which

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[Transcriber's Note: This etext was produced from

I happened to be studying a Cepheid variable star—one of the huge,

I ignored him and turned my attention to Army business. The

"Can it be ... people?" Rizzo wondered. His face, bathed in the

When we woke up, the oscilloscope trace was still quiet.

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