VIEWPOINT

2023

E YES IN THE SK Y:
OPPORTUNITIES FROME A R T H O B S E R VAT I O N
Making space for satellite opportunities
in private and public sectors

AUTHORS
Mario Nico

Earth observation (EO) — combined with

Giancarlo Agresti

developments in cloud infrastructure, big data,

Michael Kolk

and artificial intelligence (AI) — enables data-driven
decision-making to address societal challenges likeDario Garante

city development planning, land management, andStefano Sanna

climate issues. Higher data resolution fostered byLeonardo Toffoli

technology enhancement, increased access to freelow-/medium-resolution images, easily accessible
computing power, and accelerating data-driven
organizational transformation have enabled steady
growth in EO market. This Viewpoint analyzes
potential applications and barriers, shedding lighton how private and public organizations could
grasp value.

ARTHUR D. LITTLE

VIEWPOINT

DATA- DRIVEN DECISION MAK ING IS K E Y TOADDRES SING SOCIE TALCHALLENGES
Converging developments in cloud-based
infrastructures, big data, and AI, plus a
considerable reduction in the cost of spacesystems, have created an unprecedented
opportunity to boost the adoption of data-driven
applications in response to a variety of societal
challenges. These include monitoring scarcityof resources, mitigating climate change andenvironmental degradation, tracking migration
flow, and monitoring the acceleration ofurbanization and rural area development.
EO is a key enabler of data-driven decision-making
in this context, allowing information gathering on
atmospheric constituents and climate indicators
by cross-referencing satellite data with available

IRIDE — A new Italian EO Constellation
As part of the National Plan for Recoveryand Resilience (NPRR), the Italian
government invested approximately
US $1.4 billion to develop a nationwide
EO constellation, with completion
expected in 2026. Additional investmentsof over $75 million will be dedicatedto the development of a marketplacethat provides EO services to publicadministrations. Geoinformation services
will include monitoring of territory,landslide areas, and infrastructure.
Improved availability of high-quality EO data
In recent years, the EO market has witnessed
steady growth supported by four main drivers:
1. Higher resolution of commercial data due toenvironmental and demographic information.

improvements in remote sensing technologies

Big data collected from multiple sources,

and constellations

including satellites, can support, for instance,
migration flow monitoring and detection of illegalactivities. Furthermore, EO provides timely andspatially disaggregated information useful to city
development planning and land management.
The current low adoption of EO is due mainly tothe high costs of data acquisition and processing,
limited availability of exploitable data, shortage ofskills, and an inertia to embrace change. However,

2. More accessible commercial images coupledwith free low-/medium-resolution data postCopernicus
3. Easily accessible and scalable computing
power for data processing and analytics
through cloud technologies
4. Accelerating data-driven transformationof organizations

recent technological developments are opening

Remote sensing applications have specificup opportunities to develop and introduce newrequirements in terms of type of sensor (the mostservices (see, for example, sidebar “IRIDE: A new

adopted are optical/multispectral and synthetic

Italian EO constellation”). Indeed, following theaperture radar, while other technologies include

EU’s Copernicus EO program, several European

infrared and radio frequency), resolution, and revisit

member states have recognized the value of EO’stime (i.e., the time elapsed between observations ofdata potential and are investing in initiatives tothe same point on Earth). And historically, the costfacilitate the adoption of EO in both public andof data has been a key barrier to adoption. However,private sectors.

reductions in the cost of access to space (including
satellite manufacturing and launch costs) coupledwith better-performing sensors have increased
the supply of commercial satellite imagery while
reducing data prices considerably.

EYES IN THE SKY: OPPORTUNITIES FROM EARTH OBSERVATION

2ARTHUR D. LITTLE

VIEWPOINT

Institutional constellations, such as Copernicus

Understanding end-user needs & measuring

and the future IRIDE, which offer free EO data,

value of EO applications

have been a major step in the adoption of EO
applications, particularly those characterized
by less-stringent requirements. Furthermore,increased digitalization is promoting a shift

EO offers a variety of services for both the public
and private sectors, including monitoring of landand coastal/marine areas, meteorological andclimate forecasting, decision-making support in

toward data-driven processes and decisionmaking and enabling more widespread use of
EO data. Developments such as cloud-based

emergency situations (e.g., rapid damage mapping),and surveillance services (e.g., vessel tracking).

storage and computing, coupled with a greater

The first step in adopting satellite-based EO

availability and accessibility of innovative

services is to assess the value that such services

AI/machine learning (ML) algorithms, arecan bring. On the one hand, public and private

enabling public bodies and private companies

organizations should measure the suitability

to process and analyze large amounts of dataof EO applications by applying a multi-criteria

more efficiently. And as AI increasingly turns

system that considers economic benefits (e.g.,

into a commodity, it represents an enormous

increased efficiency through cost reduction),

opportunity for societies, governments, andsocial and environmental impacts, and regulatory

businesses to collect and analyze data from

implications. On the other hand, organizations

sources including drones, satellites, and Internet

should also consider the cost of adoption,of Things. In this context, organizations areincluding the maturity of the algorithms and costrevealing a growing willingness to create anof images. The net value to end users depends onenvironment where all business structures

the specific use case and the unique context ofare empowered to make complex data-driven

the application.

decisions and where data and analysis tools arewidely accessible across the entire organization.

In public administrations (PAs), authorities shouldproceed with periodic high-level screening toexploit emerging EO applications and perform
deep dive assessments on the most promising
ones. Figure 1 illustrates the impact and potential
applicability of EO services and satellite

Figure 1. Potential EO applicability in applications
public inadministration
the public sector.
Figure 1. Potential EO applicability in public administration
PUBLIC ADMINISTRATION SECTORSSATELLITE APPLICATIONSClimate &Agriculture environment

CultureEducation& research

HealthLaw
Social &Infrastrucenforceeconomic
ture &ment &developtransportcivil
mentation
protectionTourism

Urbandevelopment

Coastal & marine monitoring
Air quality & pollution
Ground & structural motion
Land use/land cover
Climate & hydrometeorological forecasting
Water management
Emergency
Security
Legend:Direct impact on core processes
Direct impact on noncore processes

Indirect use of EO data

Limited application

Sporadic use of EO data

Source: Arthur D. Little

EYES IN THE SKY: OPPORTUNITIES FROM EARTH OBSERVATION

3ARTHUR D. LITTLE

VIEWPOINT

Case study — Land use monitoring system to improve efficiency in checking payments
Arthur D. Little supported a local Italian

However, considering the specificities of the

public institution in defining its land-local territory and their impact on the quality

monitoring strategy and adopting a landof the algorithm outputs, multiple vendors

parcel identification system. The aim was tohave been assessed against different factors,

strengthen control mechanisms over payments

including costs, ease of adoption, andto agricultural and rural developments —maturity level. The analysis also explored theaccording to EU regulation 2021/2116 on theorganizational and operational impact, withfinancing, management, and monitoring of thean estimate of related costs. The systemcommon agricultural policy — through satellite

requires the setup of a back-office team toimaging:

review the system outputs when the results- Attractiveness. The system brings bothfrom satellite images are not 100% certain.

efficiency and efficacy benefits. It enables
the continuous monitoring of all land parcel- Adoption strategy. Different potential

adoption strategies were analyzed, including

requests as opposed to the current 5%

centralized and independent options.

random sample analyzed, while requests

Examples of some of the factors consideredthat require in-depth controls are selected

in making the final decision include

based on EO data. As a result, EU aid isreliability of the algorithm, overall costs ofmanaged in a punctual manner and paid only

adoption, regulatory constraints, quality ofwhen declarations are in line with the cropservice, as well as ease of customization anddetected.

integration. In addition, a pilot of the most-- Accessibility. The system leverages freely

available EO data from Sentinel satellites inpromising solutions on real-life parcels in
local territories was conducted.

the Copernicus constellation, which detect
crops’ growth over time according to theNormalized Difference Vegetation Index.

Ongoing barriers to adoption
Despite the growth in EO services, there are still
adoption barriers to overcome, including:-from EO-based data. Local administrations,for instance, are highly fragmented, withlarge budgets often managed by central

Lack of awareness. PAs and private

authorities, making it difficult for small local

enterprises are not always aware of theadministrations to access these funds.

numerous benefits that EO can bring in termsof savings and increased value in the deliveryof higher-quality community services.

-could face high costs in order to benefit- New skills development. New skills andcapabilities are necessary for organizations
to collect insights from EO data, ranging

Funds access. The development andfrom knowledge of satellite remote sensing

evolution of current technologies require

technologies to advanced analytics

investment in both IT systems and data-techniques. AI and ML enable raw data

governance processes in order to exploit

collected by satellites to be turned into

advanced analytic techniques based on ML/

information usable by PAs and companies.

AI algorithms. Public and private companies

EYES IN THE SKY: OPPORTUNITIES FROM EARTH OBSERVATION

4ARTHUR D. LITTLE

VIEWPOINT

Following the Copernicus constellation’s
deployment, European and national institutions
have invested in increasing the awareness of
EO’s potential and facilitating its adoption within
PAs. Yet, despite the high level of automationin the first steps of the EO value chain (seeA tailored adoption model
Three models have been identified to support theexploitation of EO and reduce adoption barriers(see Figure 3):
1. Independent/ad hoc. The independentFigure 2), human intervention and deep domain

model focuses on one specific use case (e.g.,knowledge are still required to develop and tune

land parcel identification in the context ofdata analytics algorithms. In the future, thethe EU’s Common Agricultural Policy). It is

commoditization of AI and ML could reduce theused primarily when an innovative solution isneed for technical expertise, particularly for lessneeded and cannot be found on the market orcomplex use cases, although domain-specificwhere specific local conditions require heavily

knowledge will likely still be required.

customized algorithms. In such scenarios,
the level of integration along the EO value
chain tends to be low, as it depends mainly on

EUROPEAN AND
N AT I O N A L I N S T I T U T I O N S
H AV E I N V E S T E D I N
INCREASING THE
AWA R E N E S S O F
EO’s POTENTIAL

the provider selected, which will often focus
on the analytic services provision itself. Thismodel is characterized by limited economies
of scale, as the use case is usually adopted bya single administration or private company
requiring specific customization and qualityof service.

Figure 2. EO value chain

Figure 2. EO value chain

Processing of raw images,
converting them into data readyto be used for analyses, thanks
to AI/ML technologies application

Local municipalities,
civil protection, local
authorities, police, etc.
Satellite image acquisition,transmission to data centerfor storage

Application of AI/ML
services for data analysis
to turn data into valuable
information for end users

Source: Arthur D. Little

EYES IN THE SKY: OPPORTUNITIES FROM EARTH OBSERVATION

5ARTHUR D. LITTLE

VIEWPOINT

Figure 3. EO adoption models
Figure 3. EO adoption models
INDEPENDENTCENTRALIZEDMARKETPLACE

Use case
addressed

1 use case

1 use caseMultiple use cases in 1 point

Potentialusers

1 user

Few users

Large number of users

Value chain
integration

Application/service

Application/service

Data to service

No economies of scale

Limited economies of scale

High economies of scale

Highly customized

Limited customization

Limited customization

Full control of suppliers

Partial control of suppliers

Partial control of suppliers

HighLimited

Very limited

Cost ofEO data
Customization onuser specificities
Quality of service
EO skills
required by PAHighMedium
Low

Source: Arthur D. Little

2. Centralized. Centralized models areource: Arthur D. Little

effective solutions thanks to the presence ofdeveloped for a specific application, but they

an anchor customer (i.e., the main institutional

are managed by a central entity providing anusers) and transactions directly managed byend-to-end service. The centralized model

the system. The platform offers multiple use

focuses on mature use cases adopted bycases in one single place, focusing on easily

several end users, and local conditions have

replicable services with limited customization

a limited impact on algorithms. This model

required. This model is designed to integrate

partially unlocks economies of scale on thethe entire EO value chain, from data access topurchase of EO images, and the service isfinal services. The quality of service depends

usually financed by the central authority. Inon the supplier, over which the individual user

this case, the quality of service depends onmight have limited control, as well as thethe central entity, which the individual end

capabilities of EO. As such, the level of skills

user might have limited control over.

required by PAs and private companies is likely

3. Marketplace. This model offers a digital

to be very limited.

ecosystem, including a relevant hardware and

There is no “one size fits all” adoption model.

software infrastructure, enabling the space

However, the most suitable one can be selectedindustry to offer EO data (both archived anddepending on requirements. Alternatively,on demand), applications, and services. PAsorganizations may design a “fit for purpose” model.

and private companies benefit from cost-

EYES IN THE SKY: OPPORTUNITIES FROM EARTH OBSERVATION

6ARTHUR D. LITTLE

VIEWPOINT

CONCLUSION

RE ACHING FOR THE SK Y
E O A P P L I C AT I O N S A R E A VA L U A B L E A S S E T
FOR MONITORING AND MANAGING KE Y TRENDS
Recent developments in the EO sector are opening up new opportunities forboth public administrations and private companies. EO applications are a valuableasset for monitoring and managing key trends, including urbanization, bordersecurity, climate change, air quality, and water management. To embrace EO
and accelerate its adoption, administrations and companies should take somekey steps, including:
1

Define a strategic rationale for EO implementation by evaluating possible
applications and assessing expected benefits of EO services implementation,
coupled with advanced technologies development.

2

Assess current strategies, technologies, and processes to identify gaps
and map ideal perimeters, as well as to define ideal implementation model.

3

Identify priorities that can add value to current operations while building
consensus within the institution and defining a clear implementation roadmap.

4

Identify required digital technologies and IT infrastructure to facilitate
integration of data analytics to fully exploit EO potential, while ensuring security.

5

Redesign processes, roles, and competencies required to adopt EO withinpublic or private context, defining governance principles and adapting themto new processes.

6

Define plan to build/nurture required skills and capabilities related todigital transformation and advanced technologies, as well as developingchange management activities to overcome resistance.

7Define strategy execution, engaging large panels of stakeholders involvedand leveraging the support of experts in solutions procurement, IT systems
development, and processes adoption.

8

Design monitoring process to assess gradual implementation of EO andcontinuously identify new possible adoptions.

EYES IN THE SKY: OPPORTUNITIES FROM EARTH OBSERVATION

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