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Space Applications Networking Event 2019

Session 1 – Infrastructure

10:30-12:00

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Session 1 - Infrastructure Agenda

Topic Presenter Representing

Welcome Sara Huntingdon UK Space Agency

Introduction and Overview Mike Moseley Knowledge Transfer Network

Collaborating from the ground up Matt Goodman Ordnance Survey

RESAT- Renewable Energy Space

Analytics Tool

Maria Noguer Institute of Environmental Analytics

City LEvel ANalysis for Electric

Vehicles

Ian Dee Energeo Ltd

PNT Transport Modelling Lawrence Oakes-Ash City Science

EO derived soil moisture estimation Claire Roberts NPA Satellite Mapping, CGG

Minimising Risk of Tailings Dam

Failures - Peru

Gregor Petkovsek HR Wallingford

Condition Assessment of CNI Paul Bhatia Geomatic Ventures Ltd

Panel debate and Q&A All All

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Session Chair

Mike Moseley – Construction LeadKnowledge Transfer Network

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Collaborating from the ground up

Matt Goodman

Head of Public Policy & Engagement

Ordnance Survey

The challenges and opportunities facing the

public sector dialogue on infrastructure

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More than just mega-projects

Infrastructure is an interconnected system of systems that provides the foundation for our society. It does more than just provide water, power or transport; it helps to make cities liveable, boost quality of life and fuel growth, productivity and prosperity.

Infrastructure has been recognised as one of the five pillars of the government’s Industrial Strategy, but all five are linked by a common thread.

Data is being recognised as critical – both data of infrastructure or data forinfrastructure – for both connectivity and mobility.

We curate one of the UK’s key national data assets, a database of over 500 million geographic features of England, Scotland and Wales.

Our various data capture techniques help us make 20,000 updates to our database every day.

200 surveyors use distance measures and hand-held GNSS receivers, supported by OS Net – a network of 110 receivers which means we can capture to an accuracy greater than anyone else in Britain.

Our surveying equipment is capable of achieving up to 1-2 cm accuracy

Ordnance Survey

United Arab Emirates

In the UAE we developed a prototype palm tree and mangrove counting system to monitor urbanisation. Using deep learning techniques and an interoperable data model to store the data, we tested an automated change detection system for managing natural resources. This led to faster information about the impact of urban development on the natural environment.

Case Study – United Arab Emirates

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Location data matters

ChallengesPrivacyInteroperability +StandardsAccessible vs. OpenWho pays?

OpportunitiesProliferation of sensors/dataNIC’s National Digital TwinGeospatial Commission

The Quintuplets:Innovation StrategyInfrastructure StrategyData StrategyGeospatial StrategyTransformation Strategy

Whether it is collected from orbit or built from the ground up –

Location data is the golden thread that ties infrastructure together.

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RE-SAT: Renewable Energy SpaceAnalytics Tool

RE-SAT Energy Analytics Platform – supporting Small Island

Developing States (SIDS) transition from fossil fuel to renewable

energy generation

Dr Maria Noguer and Alan Yates

Institute for Environmental Analytics

Institute for Environmental

Analytics

We turn environmental data into

meaningful information for end-

users:

• through data analytics;

• by building tailored and easy-to-

use software applications, and

• by sharing know-how.

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RE-SAT: Project Overview

• The Institute for Environmental AnalyticsLed by

• Small Island Developing States (SIDS) Target Countries

• Exploit Earth Observation and other sources of data to help SIDS increase their use of intermittent renewable energy

Aim

• Proof of concept energy-planning platform in collaboration with Seychelles

Phase 1

(Dec 2016 – Nov 2017)

• Scale up to another 6 SIDS –

• Palau, Tonga, Vanuatu, St. Lucia, Montserrat, Mauritius

Phase 2

(Jan 2018 – Mar 2021)

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RE-SAT: Simulating Future Energy Production

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RE-SAT: Satellite products used

Satellite dataset Use

Visible imagery Airbus Pleiades

Sentinel 2 products

Identification of buildings, and rooftop PV

installations in the absence of aerial

photography

Solar resource maps SARAH-E (radiation product)

MODIS (cloud climatology)

Validation of simulated incoming shortwave

radiations

Calibration of clear sky to cloudy radiation

conversions - captures the expected drop-off in

production between clear sky production and

cloudy conditions

Wind resource maps OSI SAF, AMSR-2, HY-2A and

Sentinel 1 OWI

Investigated but the spatial and temporal

resolution of these products (with the exception

of Sentinel-1) were found to be too coarse.

Wave resource maps EU Copernicus Marine

Environment Monitoring Service

(CMEMS) data

Creation of Wave power and Ocean Thermal

Energy Conversion (OTEC) resource maps

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RE-SAT: Renewable Energy Resource Maps

Tonga

Montserrat

St LuciaSeychelles

Mauritius

Palau

Vanuatu

SENTINEL 2 OPTICAL IMAGERY

RE-SAT DEVELOPED SOLAR MAP

Montserrat

RE-SAT DEVELOPED WIND MAP

We use

satellite data

and other

sources of

data combined

with advanced

modelling

techniques to

quantify solar

and wind

energy

potential by

location

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RE-SAT: Benefits and Challenges

Benefits for the SIDS:An easy to use platform to support government planners and other key stakeholders:

• Explore options for best renewable energy mix

• Plan where to locate assets

• Estimate power production taking account of environmental constraints and weather variability

• Enhanced local knowledge and expertise for RE development

Benefits for the IEA:• Increase expertise in using satellite sources to address the lack of data in SIDS

• Develop deep energy sector expertise with references and credentials for future sales opportunities

• Strengthen the IEA’s reputation at international level

• New avenues through knowledge exchange activities around the dissemination of RE-SAT at conferences

Lessons learnt / Challenges:• Timing of the project and in-country commitment

• Knowledge sharing

• Organisational structure

Unintended impact: Enhanced collaboration among government departments and stakeholders within the SIDS

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RE-SAT: Next Steps

RE-SAT is still under development

Phase 2 is scaling RE-SAT to 6 other SIDS – aiming for an operational

software platform that work across regions and with a variety of renewable

energy resources.

At the end of the IPP project we aim to make RE-SAT available via a

SaaS-based commercial licensing model to a variety of end users -

including energy companies, investors and developers – both within SIDS

and other geographical markets.

On top of that we plan to build new complimentary analytics applications

targeting the energy market.

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City LEvel ANalysis for Electric Vehicles (CLEAN EV)

Ian Dee

Energeo Ltd

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Electric Vehicle Infrastructure Issues

- EV utilisation is growing rapidly, but charging infrastructure isn’t increasing at the same pace

- Local Authorities have less budget and resource than ever before

- Identifying suitable locations for charge points is time consuming, and often manual

- Stakeholders do not have the data or evidence base to act proactively and strategically

- On-street locations are particularly challenging, considering access, parking etc.

- Within the EU, 42% of citizens reside in properties without access to private parking (33% UK, 62% Germany, 66% Spain)

- No database of off-street parking exists to facilitate ‘quick win’ strategy implementation

- Regulatory doubts regarding retrofitting of streetlighting columns

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Satellite Applications Utilised

Energeo’s Solution was to leverage the power of Very High Resolution satellite imagery and machine learning. Spaceborne imagery – in our case Digital Globe WV3 - provided an up-to-date, high resolution data source allowing us to assess the possibility of automating the identification and measurement of features and assets critical to on-street EV infrastructure roll out, such as: -- Pavements- Pavement obstructions- On-street vehicles- Off-street private parking (driveways)

Pavement width measurements

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Benefits and Challenges

Using satellite imagery and machine learning, Energeo were able to deliver a comprehensive suite of intelligence to Bournemouth Borough Council to support their EV activities, which included: -- 27,000 pavement width measurements- 30,000 points of obstruction- 44,000 driveway locations- 42,000 on-street vehicles

The data provides EV infrastructure stakeholders with an unrivalled evidence base on which to build strategic roll-out programmes, rapidly respond to resident and business requests, and apply for onward government funding such

By enabling stakeholders to act more intelligently and rapidly, the intelligence delivered can facilitate accelerated adoption of electric vehicles across all part of society, accelerating the transition to a low carbon society

Machine learning resource is an extremely rare commodity, not all public sector bodies can handle hundreds of thousands of data points easily, image processing and interrogation needs computing power . . .

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Next Steps

- Service has been launched, interest is already high and continues to grow

- End user conversations continue, and new data applications are being highlighted regularly

- Development roadmap includes grid impact analysis, point-of-destination charging analysis, and obstruction classification, further accuracy assessment

- Tentative international interests from organisations in Germany and Australia at present, and we continue to explore the global potential

- Energeo are seeking VC funding to accelerate business growth and rapidly build out products and services

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Transport Modelling Based on PNT Data

Enabling more for less, faster…

Laurence Oakes-Ash

City Science

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Project Overview

• The Hinkley Point C nuclear plant is one of

Europe's largest construction projects with

build costs in excess of £19 billion

• The original transport model cost in excess of

£1m and is captured in a report spanning

1,800 pages

• Somerset County Council & Sedgemoor

District Council wanted a way to understand

HGV movements and their impact on the

network on an on-going basis without re-

building a transport model

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Satellite applications used

• PNT Data = Position, Navigation & Timing

• The most commonly known type of PNT data

being GPS

• We were able to use GPS data in 3 key

ways:

• Tracking detailed routes and flows

• Building ‘dynamic’ trip matrices

• Combining with junction data to provide

much more detail about what is going on

in ‘real-time’

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Benefits and challenges?

Benefits:

• Flexibility: Able to provide a detailed,

interactive ‘What-if’ model to enable the

transport authority to understand HGV

impacts on an on-going basis

• Cost: Model delivered at a fraction of the cost

of traditional approaches

• Accuracy: Developed techniques to merge

datasets and provide a greater level of

accuracy than traditional approaches

• Live & constantly updated: Developed new

techniques that will enable ‘live’ digital twin

transport models

Challenges:

• Data barriers:

• People don’t know what they have

• Multiple ‘siloed’ datasets

• Commercial restrictions

• Sample size:

• Geo-spatial bias – techniques and

assumptions to manage inherent bias in

datasets

• Sample rate:

• Some enhanced features only become

available at high sample rates

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Benefits – ‘What-if’ modelling

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Benefits – Incredible detail and insight

Modelled “actual flow” (PM) versus SCOOT

From 2157 338.2 see 14121G

From 2190 558.5 see 1412H and 1412J

From 2205: 257.5 see 1412F

From 5284: 466.72 see 1412E

Figure 20: Example Cadence Node View with embedded Scoot Data – prepared for Somerset

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Benefits – Significant opportunity from existing data

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Next steps?

What happens now?

• Technology already used to provide insights and

evidence to support other local authorities. Can

support:

• Cycling strategies & monitoring

• Bus & rail strategies & monitoring

• Freight strategies & monitoring

• Journey time analysis

• Developing new features such as optimisation

strategies and automated modelling through an

Innovate UK project

• Integrating with our transport modelling software

Cadence to put the power of this data directly in

the hands of decision makers

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EO-derived soil moisture estimation

Rapid prototyping of an EO-derived soil moisture estimation algorithm using Sentinel-1 data

Claire Roberts, Harry McCormack, Rachel Holley

and Adam Thomas

NPA Satellite Mapping, CGG

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Project Overview

Aim: Improve understanding of soil moisture from Earth Observation

radar data and develop algorithms for remote estimation of soil moisture

at greater spatial resolution than currently available.

Why: Soil moisture can have significant impacts on asset management

within the rail sector.

End user: Network Rail

Support from: National Physical Laboratory,

British Geological Survey and CEH COSMOS-UK data

Study Areas: Three UK sites• Withy Beds

• Hollin Hill

• Hadlow

Image supplied courtesy of Network Rail © Network Rail 2018

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Which satellite applications were used?

EO data: Synthetic Aperture Radar (SAR) data from Sentinel-1

• 6 day repeat

• Improve soil moisture output spatial resolution (100m x 100m)

Technology:

• InSAR methods

• Algorithm development based on published material

• Technical advances in masking, de-noising and optimisation

filtering methods.

Comparison datasets: Soil Moisture Deficit (SMD) and The Centre

for Ecology and Hydrology’s (CEH) COSMOS-UK soil moisture

data.

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“A simple but effective

relative method of

calculating soil

moisture values”

Benefits and Challenges?

Benefits:

• High resolution, timely information on potential soil moisture

• Relative soil moisture estimation tailored to a client site

• Offers complementary information to rail sector

• Improved algorithm development

• New product to complement CGG’s InSAR services.

Challenges:

• Access to comparative soil moisture data

• Ground surface cover changes

• Variability over the test sites

17th January 2018, image © NPA Satellite Mapping

Background image Google

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Next steps?

Outcome:

Satellite radar data can be used to generate an improved spatial

resolution estimation of soil moisture

Next Steps:

• Demonstrate additional study sites

• Build on the algorithm to address variability and repeatability of

results

• Apply to other sectors e.g. mining

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Minimising the risk of tailings dams failures through the use of remote sensing data

Gregor Petkovsek, Alexandra Murphy,

Olalla Gimeno, Craig Goff

HR Wallingford

DAMSAT (DAm Monitoring from SATellites)

International Partnership Programme - Peru

March 2018 – October 2020

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Project Overview

Tailings dams:

❑ earth embankments used to store (toxic) mine waste and effluent

❑ can be more than 100m high, constructed using the tailings to save on costs

❑ high failure rate: two orders of magnitude higher than water storage dams

Focus country – Peru (region-Cajamarca):

❑ 200+ active mines in Peru, 1000s of inactive, closed or abandoned tailings sites

Project is looking at

❑ cost effective way of remotely monitoring tailings dams and deposits, combined with

real-time in-situ devices, analysis and forecasting tools – proof of concept

➢ provide an operational warning service

➢ support the reduction of the environmental and social impacts of failures

➢ influence the adoption of EO monitoring for tailings dams

UK Consortium: HR Wallingford, Telespazio-Vega, Satellite Applications Catapult, Siemens, University of Oxford,

Oxford Policy Managament

International Partners: FuNaInHi, National University of Cajamarca, CIEMAM, Intenational Technical Advisory Panel

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POLLUTION DETECTIONMOVEMENT DETECTION

Which satellite applications are used?

❑ InSAR – SyntheticApretureRadar Interferometry

• Movement detection, many points over a large area

• mm-cm precision

• Cosmo-SkyMed, Sentinel-1

Wavelength

Reflecta

ncy

Optical imagery

• Iron Oxide

• Vegetation health (NDVI)

❑ GNSS monitoring with receivers in-situ

• Real time, mm precision

Source: Telespazio VEGA UK

Alerts: Analyses, Forecasting, Visualisation

• Weather, runoff, pond fill rate

• Dam breach and consequences

Movement Alert level

Runoff

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Benefits so far (10/30 months):

• awareness raised regarding EO capabilities for monitoring of tailings dams through user

engagement and knowledge sharing activities

Benefits expected:

❑ Public sector/government user/wider society:

• Information from an extensive area, incl. remote areas, and with higher frequency

• Support planning and prioritising field visits – more efficient use of resources

❑ Mining companies:

• Wide area coverage – detect problems where not expected

• Possibility to take action based on forecasts, supporting the reduction of risk of failure and

damage to population and ecosystems downstream

❑ Consortium:

• Possibility to expand not-yet commercialised cutting edge research to worldwide market

Challenges:

• Sensitive sector and complex stakeholder landscape with many potential end users

• Measuring project impact (risk reduction)

Benefits and Challenges?

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Next steps?

Project in progress (10/30 months):

• Completed: user requirements and baseline, equipment assembly

• In progress: satellite imagery acquisition, development of individual methods,

automation of methods, graphical interface

• To complete: development of application, imagery analyses (baseline study +

updates)

Explore new markets:

• Expansion to other geographical areas, potential worldwide

• Expansion to other sectors – water dams

• Possibility of funding from IPP Expansion Call

More info and new updates on the project website:

https://tailingsdams.info/

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Infrastructure Session - Condition Assessment

Monitoring UK Critical National Infrastructure

Paul BhatiaGVL

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Monitoring National Flood Defences

Location of Thames Estuary Test Site

(Thames 2100 pathfinder site)

Our Aim:

To demonstrate a procedure that aids in the

management and prioritisation of site visits, in-

situ monitoring and maintenance/ renewal/

improvement of structures forming Critical

National Infrastructure (CNI)

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Deformation Analyses using Satellite Radar Imagery (InSAR)

Impose national GVL Sentinel-1 derived ground motion dataset onto national flood defences dataset to find areas of high risk.

Acquire Cosmo-SkyMedimagery over high risk areas to undertake a detailed analysis of the flood defences.

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Benefits & Outputs

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Conclusion & Next Steps

✓ We have successfully

demonstrated that the

procedure works and existing

stake-holders are impressed!

➢ We now need to:

➢ Bring more end-users on

board

➢ Attract funding and

financing to enable further

demonstrations & rollout

Contact: Paul Bhatia, CEO, Geomatic Ventures Limited,

paul.bhatia@geomaticventures.com, 07881 336841

www.geomaticventures.com

United Kingdom Relative

Deformation Map

• 2000 Sentinel-1 images

• October 2015 - October

2017

• Processed using the

Intermittent Small

Baseline Subset

(ISBAS) InSAR method

• Each pixel represents

the average vertical

height change over the

period of observations

in millimetres per year.

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Panel Debate and Q&A Session

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Announcements

Lunch is served - lower ground floor

- Due to access constraints, please keep left on the stairs

- Feel free to come back upstairs and visit the market stalls

Next Sessions: 13:00-14:00

Ground floor - Space for Sustainable Development

Lower ground floor - Marine & Maritime