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Empowering the Energy sector to optimise natural asset management

Applying AI and machine learning in a meaningful way

Did you know that there are over 4,500 satellites orbiting the planet? That’s a lot of data being streamed back to earth in the form of optical images, meteorological, radar, thermal, hyper spectral, near infrared, digital audio and video.

It’s true to say …” data happens… Everywhere, Everyday”

And what we do with this data can be critical to the proper and effective management of our natural resources, our forests, crops, water resources and effectively our population.

GSI was formed to address the issues of how best collect, curate and qualify the vast amount of geospatial data available and to refine it for the benefit of natural asset managers. This has resulted in a technology solution that can provide the deepest insights imaginable into natural asset inventories and changing land use.

This resonates particularly well in the Energy sector which is acutely conscious of its impact and reliance upon the natural environment to locate and maintain its energy infrastructure, as well as to maintain a safe and dependable energy supply. It is now possible to monitor changes in land use, encroachment and the height of forestry adjacent to critical infrastructure remotely, continuously and yet, in a cost-effective way.

Carbon offsetting is another specialist service, which requires a focused and solutions-based approach to assist companies to optimise their CO2 strategies.

So, the energy sector needs to know what is happening throughout its sphere of operations, because it has a global footprint whilst providing a reliable, cost effective and sustainable service to society..

And because all this happens, Everywhere Everyday, you need to know about it.

GSI monitors, qualifies, analyses, evaluates and alerts their subscribers in near real-time about the issues that might have an impact on their operations.

Because you never know when you’ll need that help to prevent, avoid, eliminate or mitigate the effect of issues that impact operations. From forest encroachment on utility lines in remote regions to illegal construction in protected areas or the need to properly account for land classification during infrastructure planning or evaluating the CO2 stock measurement from an investment, there is a growing need for GSI analytical insights.

A deep scientific know-how of how data can be transformed into value.

GSI has developed a comprehensive portfolio of valuable intellectual property focused on developments in geospatial image processing and analytics. This IP drives a multitude of innovative solutions from GSI which result in optimised asset management at a global scale. The GSI data refinery is the ecosystem that consumes data and delivers valuable insights that drive imprved decision making for natural asset managers globally

On its own, crude oil is of limited utility. It needs to be refined, treated and mixed before being transformed into one of the many different products we take for granted, like gasoline, heating oil, chemicals, plastics and pharmaceuticals. And it’s not easy. It’s complicated and requires lots of in depth knowledge and investment.

That’s why, for example, there’s only one major refinery in the whole of Scotland at Grangemouth

Raw satellite data works the same way.

On its own it has limited use.

GSI has created a powerful data refinery that treats raw satellite data with powerful algorithms, mixes it with a variety of other data-sets and, using advanced artificial intelligence and machine learning tools, refines out a wide range of highly usable insights that can be critical for effective decision making for optimised natural asset management.

And like refining oil, it’s not easy. It requires a deep scientific know-how of how data can be transformed to value.

These insights are delivered either on the GSI Ethos - the AI platform for earth observation asset management or via API calls for companies who require the insights to be published on their specific platforms.

Some illustrative examples

Tree encroachment on power lines

Tree encroachment on power lines can be a dangerous and expensive occurrence. This can be difficult and dangerous to monitor particularly in remote regions where pylons may be situated many kilometres from the nearest access road.

GSI’s land classification insight permits utility companies to not only identify if there are any encroaching trees but using their advanced AI tools GSI will also identify the height of the encroaching trees to permit the operator to ascertain the actual threat level.

In the illustration, power lines are clearly visible as is the fact that trees are growing directly under them. GSI and its Ethos AI platform was able to not only detail this classification but was able to determine the canopy height in the growth limit zone.

Of note is the analysis of the canopy height for trees growing directly below the power cables in what is obviously valley over which the lines are suspended.

In the growth limit zone within the forest the average canopy height was calculated to be 7.4m high and directly under the lines in the valley to be 20m in height. This level of detail is of high importance to the utility operator and the forest owner who may have responsibility for managing tree growth in the growth limit zone.

This is practical and critical AI technology at work delivering significant added value to the operators.

Measuring biomass & carbon stock

Carbon offsetting is relevant to some energy companies and being able to evaluate how effective an investment may be in a carbon management program is of critical importance to those energy company executives.

Particularly in the area of biomass and carbon stock levels in forestry investment programs,

GSI’s Ethos IaaS platform continuously measures forest inventory attributes such as biomass, basal area, trees per acre, tree volume and height, anywhere in the world.

This allows both regional and global operators to not only establish a baseline for their carbon management programs but also to obtain, in near real-time, changes to natural assets, whether through harvest, planting, encroachment, damage or disease to produce robust analytics on the rate of carbon abatement achieved by specific forest carbon initiatives.

This may can have significant impact on the validity and effectiveness of the carbon management programme by providing operational insights to improve the performance, measurement, and verification of offsetting schemes.

Such analytics improves the operator understanding of both carbon activity as well as risk of fire due to the high levels of dead biomass in the zone. Such analytics can have a major effect on risk mitigation of the investment.

Again, GSI is able to offer a practical, commercialised solution

for improved carbon management and risk mitigation which could significantly improve the operator’s financial results.

Assessing wind turbulence for optimized turbine output

Wind turbine blade efficiency and wind flow over them are critical elements in the overall operating effectiveness of any wind farm. Turbulence can also place undue stress on the generator transmission systems resulting in premature failure or bearing damage.

Operators are careful to install their turbines in areas that offer the maximum output efficiency,

But there could be instances where turbine efficiencies may be negatively affected by high structures, generally trees over 10 m in height that can disrupt the smoothness of the airflow over the turbine blades- even up to 1 km from the turbine tower.

GSI's ETHOS IaaS platform powered by its AI and machine learning technology continuously measures tree height and location using open satellite data.

As can be seen in this example, the wind turbine in the circle is located close to a forest. GSI and its ETHOS platform analyses and reports on the attributes of the forest that may have a negative effect on the turbine efficiency- in this case, tree height.

GSI ETHOS creates a report describing the profile of the forest in the immediate vicinity of the turbine that may impact smooth airflow. As can be seen, the average canopy height of the forest stand being analysed is over 14m. This may affect the turbine efficiency.

Wind farm operators can apply the results to their computational fluid dynamics –CFD – modelling systems to minimise topographical energy yield prediction (P50) uncertainties. This allows for highly precise forest management to optimise any felling programmes while ensuring the tree species are fully characterised in compliance with the site’s habitat management programme

In the Energy sector, change in natural asset use can have significant impact on operations and GSI has developed their range of Energy insight solutions to assist companies in maximising their investments and optimising the use of their natural assets.

GSI also has a wide range of additional insight solutions that focus on the Forestry and Agriculture sectors with applications in the insurance, retail, government and drone markets.

For more information on how GSI can assist you in protecting your natural assets, please connect with us:



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