Whether it is effective analysis of electric vehicle charging points in Bournemouth, analysing litter off the coast of Cornwall or monitoring our Critical National Infrastructure assets, satellite technology has an increasing role to play in delivering public sector services and helping us measure policy outcomes. Following a successful open grant call for good ideas, the UK Space Agency has awarded grants to eight projects totaling ~£0.7M, to test whether space can help meet existing and emerging policy challenges.
The UK Space Agency’s Space for Smarter Government Programme (SSGP) exists to help civil servants embrace space.
Satellite data and applications which utilise space have the potential to change the way government works and be a lever for wider space sector growth, in line with wider UK targets. As well as providing training and enabling access to data, wider expertise and market capability through events such as our annual Showase, SSGP also works alongside the public sector, academia and industry to demonstrate the art of the possible. By working alongside departments, SSGP helps to explore and de-risk some of the initial R&D involved in bringing a new satellite application into operational use to meet a public sector need.
As well as helping DEFRA and its partner agencies embed using satellite imagery as part of its policy delivery to look at topics such tree health, land cover, peatland monitoring, SSGP has been working alongside key organisations including the Natural Hazards Partnership, the UK Environmental Observation Framework, Civil Contingencies Secretariat, Emergency Services, Nuclear Decommissioning Authority and a range of Local Authorities to demonstrate what space can do for government use. With an already impressive range of case studies which tackle public sector challenges, which are available for anyone to view, the latest round of projects encompasses a wide range of new public sector challenges and stakeholders from across the Devolved Administrations and a at local and a national level.
The UK Government’s announcement in 2017 that sales of new petrol and diesel cars will be banned by 2040 has brought the need for our towns and cities to be prepared for wide scale utilisations of electric vehicles (EV) to national attention. The growth in popularity of EVs has been steady in recent years, with market share growing by 1.3% to 2.9% in the UK in the two years to 31st December 2017. This slow but consistent growth has resulted in an allowance for EV infrastructure to be implemented reactively, and often at relatively small scale.
The ban on new petrol and diesel vehicles has brought into sharp focus the need for strategically implemented assessment of current and projected future EV requirement, and for proactive roll out of EV infrastructure, especially charge points. This strategic assessment and implementation will be especially necessary across urban environments, where changes and upgrades to existing transport and energy systems will be highly complex.
Energeo’s CLEAN EV service will utilise geospatial Big Data (such as satellite imagery), Open Data, and Machine Learning in conjunction with strategic textual information to deliver an interactive, web based tool, designed to support roll out of EV charge points (EVCP) infrastructure in complex urban environments via intuitive visualisation, contextualisation and analysis of data in a map based user interface.
Via CLEAN EV Public Sector end users will be able more efficiently identify charge point requirements via visualisation of different features and influences on EV roll out, such as existing charge points, residential driveway availability and size, and footpath width and potential obstructions, in order to provide an accurate overview of current and potential EVCP preparedness. In addition, users will be able to identify specific features manually through the map interface, or run queries in order to model pre-determined scenarios that will support the strategic decision making process.
On screen content can be captured and exported as imagery or in a database format, to facilitate information dissemination and sharing, and inclusion in reports or supporting policy and strategy documentation. In addition, the utilisation of geospatial Big Data, Open Data, and Machine Learning affords Public Sector bodies the opportunity to showcase an embracing of technology in support of important strategic decision making processes.
The web based nature of CLEAN EV also facilitates the creation and implementation of a public facing version of the tool, with a focus on information display, to allow citizens to engage in the process of proactively planning and implementing EV infrastructure.
By collating, creating and integrating data from a number of sources, and delivering in a way that facilitates analysis and interrogation by the end user, Energeo believe it is possible to build and provide a tool that can be utilised by any public sector body involved in EVs and the roll out of associated infrastructure – whether in the UK or overseas.
This project takes a range of PNT data and assesses the feasibility of their use to provide accurate and informative outputs within transport planning that are robust, fit-for-purpose and meet the needs of the local authority. The project will investigate issues such as the scheduling and impact of HGVs, the effects of congestion on bus schedules and the distribution of journeys during different parts of the day or year. It will do this by aggregating GPS data, other sources of PNT data and integrating it with traffic simulation and modelling modules. The outputs will be a data-based understanding of existing and future pinch points across a range of transport modes.
The project addresses how air pollution exposure affects wide swathes of public sector policy and programmes. Management and mitigation of adverse air pollution exposure, by integrating space-enabled technologies with other public-sector initiatives, offers prospects for smarter, more efficient operations, risk reduction and enhanced policy delivery in many key areas, notably healthcare and urban air quality management.
Geospatial Insight Ltd will lead a closely-knit team of highly innovative SMEs to work in partnership with leading public authority stakeholders to demonstrate beneficial disruptive interventions in primary and secondary healthcare provision and urban planning through management of adverse ambient air pollution.
The team will employ dynamic exposure mapping to identify air pollution hot spot zones and pollution exposure levels for two demonstration projects, in Bicester and Belfast. Health risk zones will be identified. Choices of alternative routing with lower exposure risks will be notified. Data sets will be provided to public sector end users via dashboard interfaces. Healthcare professional and cohorts can use mobile apps to manage their healthcare activities where avoidance of high air pollution exposure is desirable.
A leading healthcare professional who is supporting this project states that “At present there is not a tool available to health care practitioners or the community to indicate how clean and healthy the air is. Equally, there is no indication as to where and when particular locations could be avoided to reduce the risk of being exposed to the damaging effects of pollutants.” Potential benefits for the public sector through uptake of outputs from the project demonstrations will go a long way to solve this dilemma. Uptake of outputs from the project demonstrations also offer potential benefit in policy planning and delivery at district, county and unitary local authorities in Great Britain and Northern Ireland in areas such as air quality management, transportation, land use planning, environmental protection etc.
The cost to Government of health damage due to exposure to particulate air pollution, alone, is estimated at over £16 billion annually. Managing air pollution exposure effectively and efficiently offers the prospect of significant public-sector cost savings too.
Digital charting is used extensively for navigation and high-assurance “safety of life at sea”. Due to the tight configuration control of digital systems and the infrequent refresh schedule of cartographic charting information, highly dynamic layers of information are currently omitted. Unknown and uncharacterised traffic, however, increases risk and uncertainty for high-value vessels, especially when they are required to transit congested waters where regulations may be lax or unpoliced.
Existing services such as Automatic Identification System (AIS) can be used to build up a picture of larger commercial vessel movements around the world but smaller vessels and private leisure craft patterns of life are less well understood. These smaller vessels can be seen on satellite imagery, with their wakes potentially indicating directions of travel and speed. Our novel approach involves the automatic mining through archives of satellite imagery to build up a picture of these patterns of life. The main advantage of this approach is identifying all objects of a certain size, irrespective of their type or if they are broadcasting an AIS signal, and therein delivering value-add information to existing maritime products and services.
Satellite data sources provide a timely, cost-effective way of surveying historical marine activity in a non-interference way, which can be processed into predictive analytics. The key challenge and focus of this feasibility study is to quickly and accurately select and process a large volume of data (typically hundreds of images from a range of sensors) and customising the visualisation of derived metrics in ways that are appropriate and intuitive for users. This study will concentrate on four geographic areas, selected and prioritised by the public-sector user.
Typical outputs from the process will answer questions such as:
Many users of our proposed service will not be typical users of Earth Observation data for navigation and Position, Navigation, Timing (PNT), so a new and independent data source will add real insight, as well as exposing and developing new markets for satellite-derived information.
The United Kingdom Hydrographic Office is the public-sector partner for this study. Within UK waters, additional beneficiaries could be, for example, Maritime & Coastguard Agency and Border Force. For overseas territories beneficiaries may include the Foreign & Commonwealth Office. Non-Government agencies who can benefit from this are: maritime charities such as RNLI (predicting where and when clusters of leisure users typically gather), Harbour Masters and Port Authorities (understanding where small vessels operate, creating potential hazards in their approaching shipping lanes), insurance companies (better understanding risk amongst smaller vessels), hydrographic survey companies (for planning safe survey operations when waters are less crowded) and disaster recovery NGOs (knowing where potential local volunteer private vessel operators may be able to offer assistance).
With over 80% of global GDP generated in cities, forecasts indicate there will be 30 ‘mega cities’ worldwide by 2020. This implies parallel investment in underlying infrastructure at the same pace, or greater, in order to provide the improved quality of life and sustainable living that urbanisation promises. With such extreme geographic concentrations of economic output and population, there is a growing need for infrastructure resilience as the consequential impact of loss of service becomes proportionately greater.
This project directly addresses the increasing pressures on critical infrastructure through a new space-based methodology providing regular remote assessment of structural condition for CNI asset owners. It will provide early warning indicators of potential engineering failures through absolute motion vector mapping over an asset. The business attraction of a more accurate forward-looking assessment is the significant cost reduction of targeted preventative maintenance compared to reactive corrective maintenance, which can be up to six times more expensive. Asset owners also benefit from reduced service disruption, plus associated adverse revenue impact and better Health & Safety compliance. Current assessment methods are also systematic and hence very manually intensive and costly, which this project seeks to reduce by identifying those assets requiring targeted inspection.
The service uses GNSS corrected geodetic control (OS Net network of passive reference stations) to ensure relative InSAR derived terrain deformation and motion rates are output as absolute units of measurement relative to a known datum (ETRS89). Creating uniform and hence regionally comparable risk assessments will enable fully objective risk assessments, aligned with national condition assessment standards to allow asset maintenance prioritisation at a national scale. Being independent of asset type, the technique can be applied to all forms of infrastructure deemed to be critically important, such as power stations, railways, roads, gas storage facilities, runways, flood defences and water treatment works. The wide applicability of the technique in the public (and private) sector is a key attraction for user uptake. With an estimated global infrastructure investment of £25 trillion required over the next 20 years to ensure sustainable urban futures, this project has enormous revenue potential.
The methodology will be tested on flood defence assets and through stakeholder engagement with nuclear decommissioning it will illustrate the cross-sectoral application of the proposed service.
The project is working with the Environment Agency on its Thames Estuary Asset Management (TEAM) 2100 project and their prime contractor CH2M Hill who are responsible for asset flood defence risk assessments. TEAM 2100 sets out how 1.3 million people and £275 billion worth of property will continue to be protected from tidal flood risk from defences that were built more than 30 years ago.
The methodology uses a nested InSAR approach based on initial wide area, medium resolution screening to identify relevant site criticalities. Using national threshold standards it will target higher resolution deformation studies. From the HR an asset risk index will be developed so that InSAR techniques that can be applied on soft and hard targets can be used to inform asset condition across asset types.
GeoInt Service for Marine Litter is a multi-purpose platform developed by ARGANS Ltd that aims to integrate various data sources namely satellite, drones and crowd-sourced data on a single platform. These datasets are complimentary and cover different aspects of information about marine litter. For instance, satellites can provide affordable synoptic data over large areas with a frequency in time and spatial coverage that would be difficult or impossible to obtain by other means. Drones are a very interesting complement to satellites, having an important role to survey specific sensitive or vulnerable areas. They are also good candidates to evaluate the effect of remediation and preventative actions in affected areas. The participation of citizens by crowd-sourcing is also instrumental for these goals. Direct observation and reporting is the most immediate data source about presence of marine litter and its type and to evaluate the immediate effectiveness of actions taken.
Combining this data with accurate positioning and time, and applying Big Data techniques, this service becomes a GeoInt technology that will generate near real-time information for early warning of marine litter events. It will also support the mid and long-term monitoring of these issues over specific areas. In addition, global information will be processed to identify the level of risk in different regions under study and to create predictive models that take into account natural phenomena or human activity to determine the probability of having a local marine litter event requiring intervention.
The service will be accessible to the public, voluntary and private sector with specific interest on the topic. The advantage of the service is the integration of varied data sets with very different characteristics and its ingestion to produce comprehensible reports with higher accuracy and applicability than existing alternatives, as information is currently scattered, not well classified and lacking standardization. This can lead to confusion during its interpretation and is one of the current challenges facing decision makers.
The desired impact is a better management of the marine litter issue. This will help to protect and promote tourism, increase economic productivity, and protect natural environments. The GeoInt platform will also be a useful tool to support environmental awareness and education. The information will help initiatives and campaigns seeking better and more responsible consumption. Over time it will also monitor the effect of reduction in plastic consumption as observed in marine litter.
The participation of citizens, NGOs, public organizations and private entities into the service will lead to faster responses and more efficient use of the resources to tackle the issue of marine litter, therefore supporting smart government.
The current project aims to cover the feasibility study (Phase I) for the use of satellite derived data and the implementation of the GeoInt platform.
This project aims to help the public sector to increase the uptake of satellite and Earth Observation (EO) data via sustainable ‘live’ services relating to surface water flood forecasting, and associated dynamic mapping of changing patterns of green space in urban areas. Our goal is to enable smarter, more efficient operations, to reduce risk-cost and enhance the quality of decision making within the public sector – in real-time. To keep technical risk to a minimum, and ensure measurable benefits are delivered to stakeholders rapidly, this demonstrator focusses on the integration of existing, tried-and-tested, space-enabled technologies in a modular, scalable fashion.
A novel aspect of the project involves utilising Artificial Intelligence (AI) based on social media posts (Tweets) which compare in situ reports of actual flooding from the public, with the outputs of the predicted flood modelling. This will reduce uncertainties around the interpretation of the model outputs.
The Environment Agency (EA) estimate that around 3.8 million properties in England are susceptible to surface water flooding. Economic impact from flooding of critical assets, and transport routes is considerable, especially within major cities. The risk of heavy rainfall is rising as climate change leads to more intense, more frequent rainstorms (the Met Office has reported that extremely wet days have become more common). Further, urbanisation is exacerbating both the propensity for flooding and its potential impact, so changing patterns of land cover due to development must also be accounted for (this can, for example, reduce infiltration rates).
Mature river and coastal flooding early warning systems exist in the UK, but precise warnings do not exist for surface water. Accurately predicting pluvial flooding is notoriously difficult, as it can occur anywhere. The Space Application technologies of (i) Earth Observation (high resolution optical and SAR-based imagery); and (ii) position, navigation and timing (PNT) – in the form of geo-located social media datasets - will be utilised, in combination with AI, automated feature extraction, dynamic mapping, and live flood forecasting – delivered within an easy to use ‘smart’ GIS front end. PNT, used in combination with new sources of high resolution satellite imagery, flood forecasting and AI is novel, and has the power to add considerable value to existing, fragmented data products and services and improve the efficiency of public sector asset management, decision making, and emergency response.
The demonstrator will cover Greater London (M25), with the deliverable of an operational SaaS-based system within the project timescale. The system will enable multiple users and use-cases within the same environment via the provision of bespoke-alerts and layer control. This will allow enhanced predictive accuracy, more timely and precise emergency response, collaboration and communication between public sector stakeholders (such as DEFRA, Transport for London and the Greater London Authority). The project also aims to support monitoring commitments as part of the London Environment Strategy and we have received support from the above-mentioned organisations in the form of background data and technical staff input to refine the use cases and precise system functionality.
Soil moisture can have significant impacts on asset management and operation. In the rail industry, the occurrence of earthwork failures have been shown to correspond with ground (soil) saturation and weather conditions reported at the time of failure. Positive correlations have been observed between periods of heavy rainfall and ground saturation, although it is worth noting that not all earthworks respond in the same way when exposed to the same conditions.
Network Rail face significant earthwork challenges across the 190,000 assets that they manage. Their asset portfolio traverses different terrains and environments, and therefore presents a complex range of issues that are exasperated by aging infrastructure, land-use, unknown geological hazards, third-party activities and climate change.
The Soil Moisture Index (SMI), which has been developed between Network Rail and ARUP, is currently used to estimate soil capacity at different depth levels, encompassing a range of 0 to 255cm. Maps are presented for these SMI intervals at a coarse 1km resolution. The SMI is an important part of Network Rail’s risk management.
Earth Observation (EO) experts within CGG’s NPA Satellite Mapping group believe that soil moisture estimation algorithms developed for use with Synthetic Aperture Radar (SAR) data, and specifically open-access Sentinel-1 data with its 6-day acquisition frequency across the UK, open up the possibility of remotely estimating soil moisture at higher spatial resolutions. It is hoped that this capability will provide actionable information to Network Rail, either as a confirmatory dataset used alongside the SMI, or as an extra input into the SMI itself.
There is a large difference in the dielectric constant of water (~80) and dry soil (~4) at microwave frequencies. As a result, backscatter from SAR will be sensitive to changes in soil moisture. The amplitude of SAR backscatter is also dependent on surface roughness, resulting from vegetation cover, soil texture and topography, and these will be important considerations when developing the algorithm.
CGG’s Rapid prototyping of a EO-derived soil moisture estimation algorithm using Sentinel-1 data project will involve leading SAR experts, who between them have decades of experience in deriving actionable information from SAR data collected in offshore and onshore environments.
Varying levels of spatial resolution outputs will be tested but with an expectation that there will be a trade-off between measurement accuracy and resolution. Advanced filtering methods developed for satellite InSAR processing will be applied to minimize the speckle noise present in SAR data, thus improving data quality. The applicability and accuracy of this approach is expected to vary with ground cover, with bare-soil more optimal than dense vegetation.
To successfully test the outputs from this feasibility study with respect to Network Rail’ SMI, NPA will work with the National Physical Laboratory (NPL) and the BGS to correlate and ground-truth the data.