Eyes in Sky: How geospatial finance is transforming our understanding of the earth

🛰️In 1957 Soviet Union shocked the world by launching the first satellite, Sputnik into orbit. Since 1957, around 11,000 satellites have been launched into space by an ever expanding list of countries (Nigeria was the latest to start a space program) and private companies. The recent emergence of low cost and orbit “nano-satellites” (those weighing between 1 – 10 kg) and crucially costing less than half a million dollars has rapidly expanded the market.

Falling costs

📉The lower cost of launching Nano satellites has in turn brought down the cost of satellite imagery. Previously satellite images were largely the domain of the military, NASA and other specialist agencies. Now satellite data is widely accessible to the public and private sector.

🌍Each satellite is another set of eyes on the earth. In combination with terrestrial and aerial sensors we can observe the planet and all its vital signs at a lower cost with more accuracy than ever before. For many this means being able to navigate traffic or look at houses on Google Earth. Others soon realised the transformative scientific and business potential of satellite imagery.

🕶️🔎Satellites provide an accurate, up to the minute view of what is happening to land, sea, rivers, crops, cities, and every other part of the globe. With the aid of computers and machine learning this information can be transformed into valuable data sets. But what can you do with this data?

SustGlobal

I was fortunate to speak with Josh Gilbert CEO who co-founded Sustglobal, 3 years ago. Sustglobal’s AI-powered geospatial platform “empowers you to quantify climate-related risk and capture sustainable value”.

In order to provide useful data to companies Sustglobal first had to tackle a major problem. Namely how to transform petabytes of raw data from satellites into a useful financial signal.

Satellites can monitor dozens of climate data variables, but the quantity of data can be overwhelming. This is where machine learning techniques can be used to handle large amounts of data and help turn them into a useful financial signal.

Satellites monitor greenhouse gas emissions such as carbon and methane. This lens can be applied across widely dispersed mines, factories and power stations to provide company wide analysis of total emissions.

Sustglobal have been working with the European Space Agency to monitor the greenhouse emissions of commodity suppliers such as mining firms. Monitoring these emissions allows independent verification of whether environmental targets have been met.

When a mining company states that it has cut carbon emissions in its mining operations, rather than relying on a report from a company, the claim can be checked independently using satellites.

This kind of accurate and up to date information is crucial for the successful implementation of any potential carbon tax. Governments will need accurate up to date information on emissions levels in order to assess tax levels.

Disaster Recovery

🔥As the number and frequency of wildfires, floods and extreme weather events rises driven by climate change, satellite imagery can provide a real time picture of extreme weather events which can be invaluable in responding to disasters.

👩‍💻Real time information can also be combined with historical data to create scenarios of how these climate risks will unfold in the future. This information is increasingly important to banks and other companies who wish to understand their exposure to climate risks.

Climate Risk

Climate Risk has a data problem. It is impossible to see into the future, but knowing how and where extreme heat, sea-level rise and flooding will occur is central to this new field.

🛰️Satellite data can be transformed into a powerful financial signal. Satellite analytics can show where natural disasters such as flooding or wildfires are more likely to occur. This can be overlaid with information on company assets such as crops, mines, factories and housing. This way firms can understand which assets are threatened.

🏦Banks, insurance companies and the corporate sector are eager to learn how climate risks will impact their portfolio of assets. The emergence of the Taskforce on Climate Related Financial Disclosures (TCFD) has shifted perceptions and within a decade measuring climate risk will be a fixture in every major company.

🌊Gathering granular data on how company assets will be impacted by sea level rise, fires, floods will become standard. Right now there is a great deal of uncertainty around how accurate climate risk models can be. But it is certain that demand for climate risk data is only going to increase.

While it is impossible to know exactly how climate risks will play out in the future. Predicting where a devastating fire will break out or how long a drought will last is very difficult. But it will be possible to create more accurate scenarios of how climate risks will play out in reality.

Foliage Detector

🦅Enjoying a bird’s eye view of the world also means that satellite data can be used to monitor crop coverage and estimate yields. In a similar fashion forests can be monitored to measure deforestation, forest fires and vegetation and even the risk of trees falling onto power lines and causing outages.

This was satellite data can act as a referee for environmental governance. When companies state they are committed preventing deforestation or that they are not building on land which is exposed to climate risk.

These claims can now be tested using satellite imagery. The rise of ESG reporting been tainted by claims of greenwashing, that companies are hiding their true environmental impact.  Satellite data can back or disprove these claims or dismiss shaky assertions with real time evidence from the ground.

Verifying Supply Chains

Understanding complex supply chains is increasingly important for ensure high Environmental, Social and Governance (ESG) standards.  The supplies of raw materials, labour and manufactured goods is crucial. The extraction of metals, minerals and other natural resources which damage the environment could tracked by satellite. This could give assurances about the quality and integrity of supply chains.

Consumer goods giant Unilever has piloted satellite technology to ensure its suppliers are not contributing to deforestation. By taking GPS data and watching for movement around

Conclusion

Mainstream geospatial capabilities enabled by space technology and data science into financial decision-making globally. The emergence of this field is likely to spark many new products, innovations and companies.

What is especially exciting about geospatial finance is that it is still in its infancy. Geospatial data can still still be used for many applications where understanding changes in the natural world or built environment are important.

Some commentators have compared Geospatial data to mapping the human genome. Instead of mapping humans genetics satellites can comprehensively map and monitor the earth and how humans are changing it to an unprecedented degree.  

The Machine Learning Revolution: How it can be used in the fight against Climate change

2020 was a devastating year for flooding across Africa. The Nile rose to its highest levels in half a century and Ethiopia and Sudan saw large areas swamped with water devastating farms and rural areas. Many African cities are ill prepared for natural disasters and with the continent’s urban population rising fast floods will increasingly devastate cities as well as rural areas.

As well as washing away crops, homes and livelihoods, floods can bring malaria and other water borne diseases to a weakened populace.

Climate change is making a hotter and wetter world with more unpredictable rainfall a perfect recipe for devastating floods.

The floods that shocked Germany in the summer of 2021 were proof that rich countries will not escape climate risks either. But wealth and technology can play a role in adapting to and measuring climate risk.

The World Observed

Across the world thousands of earth observation instruments, satellites, sensors and cameras are constantly collecting millions of points of data. This data includes temperature, greenhouse gas emissions, polar ice melt, wildlife statistics, forest cover (often using LIDAR which measures the density and carbon content of a forest) and lots of other useful information.

These critical indicators help capture the sadly declining health of the natural world and spiralling climate risks. But the enormous quantities of data collected by these instruments can appear overwhelming.

However, as the world is discovering machine learning techniques can be applied to the data, allowing organisations to sift through the information and turning into useful indicators.

Machine learning is a branch of artificial intelligence which uses data and algorithms to imitate human learning and therefore improve in efficiency over time.

Three Magic Ingredients

The three ingredients which make this possible are the collection of huge amounts of data via sensors, satellites etc, allied to powerful computing power which can handle the data and the application of machine learning systems which can improve the way in which they collection and interpret data.

Machine learning is a sub-division of artificial intelligence, and it means that systems can learn and improve the way in which they collect and interpret data independently.

Satellite data can used to track climate risks like drought, flood and deforestation which can be transformed into financial indicators with the help of machine learning helping firms monitor their risk profile.

Machine learning can be used to sift through the information provided by camera traps in wildlife reserves. Trailguard cameras use AI to spot different species and send an alert when the camera detects humans (or more importantly poachers) rather than animals.  Within minutes of poachers entering one of Africa’s wildlife reserves wardens are alerted and can respond to the threat.

Climate is Data Problem

Machine learning techniques are being deployed in an ever-wider number of settings.

Google have created the Environmental Insights Explorer which monitors the carbon dioxide footprint of buildings and transport networks allowing users to calculate their carbon footprint.

Satellite technology can monitor large scale carbon emissions, while sensors collect energy use data in buildings which can be used to optimise heating and cooling systems.

Climate risks are now a stark reality and machine learning can be used to help the world adapt. This could be through using data to predict floods or weather changes more accurately. Machine learning can also be used to effectively forecast energy usage to optimise renewable usage.

What Does the Future Hold

Identifying the scale and intensity of climate risks is a major challenge for governments and companies. Banks and insurers want to understand the physical risks to their portfolios. In other words: how much and where will sea level rise, how and where will extreme weather and wildfires impact on their assets on a granular level.

This is a problem for machine learning – collecting billions of points of data about the land, climate and sea and using them to accurately predict the future.

For example, researchers from Montreal Institute for Learning Algorithms (MILA) simulated what would happen to homes in Canada after damage by intense storms and rising sea levels. The objective is to try and make the risks of a changing climate real to people and businesses and provide them with actionable information.

How Can Machine Learning Aid the Battle Against Climate Change

Machine learning (ML) is particularly well primed to help the battle against Climate Change. Below are just some of the many methods and sectors Machine Learning can be harnessed to help mitigate, measure and adapt to a fast changing climate.

Energy Usage

Machine Learning (ML) can cut the leakage of methane through the monitoring of seepage data from pipe sensors and satellite imagery. ML can also be used to pinpoint where repairs are necessary in electrical infrastructure, cutting wastage and therefore increasing energy efficiency. However, ML could help the fossil fuel industry by making oil and gas more efficient and cheaper to extract and emit more fossil fuels.

ML can be used to model carbon emissions and energy mix helping planners to optimise the usage of renewable sources.

For countries without universal electricity ML can identify which electrification methods would be most suitable for a particular region. Normally this could require intensive resource heavy surveys. ML can use satellite imagery to speed the process up.

Transportation

Transportation is the source of around a quarter of global greenhouse emissions and represents low hanging fruit in terms of decarbonization. Cars and planes are the usual villains in this sector, but cargo ships are a major source of emissions.  

There are four ways in which to decarbonize the transport sector:

  • Reducing transport activity
  • Improving fuel efficiency
  • Switching to Alternative fuels
  • Moving to transport alternatives (car to train)

ML is in a prime position to drive these strategies. Firstly, ML can be used to collect large amounts of data about transport habits and patterns. For example, traffic can be monitored, and models created to forecast future demand which can help drivers and planners avoid congestion.

Predicting public transport usage and aeroplane take off times can make for more efficient, less energy using and time wasting travel. Similar techniques can be used for freight, using ML to consolidate trips to avoid empty trains/lorries and ships therefore driving efficiency and cutting the number of journeys.

Driverless cars driven by artificial intelligence are a particularly controversial transportation topic. In theory using driveless vehicles would be more efficient and safer (taking the quickest routes) and would free up time for humans.

However, this technology is unproven at scale and there remain many ethical concerns about the use of these vehicles, primarily who is responsible when things go wrong. The driver, car company or the programmer.

Electric Vehicles (EV) by contrast are now a familiar sight on the roads in many countries. ML and EV dovetail in climate friendly ways. Manufacturers can monitor EV and use ML to predict faults, battery management and usage. Over time as more drivers use EV more data will be collected on their usage. This can be used to improve performance by proactively spotting fault and identifying battery state degradation.

Cities and Buildings

Building, cities, towns and workplaces are a major source of carbon emissions. But they also offer some of the easiest fixes. Many modern state of the art buildings consume virtually none or no energy. Smart buildings using sensors and control systems can monitor energy usage identifying where more is required and using tech like windows with built in solar panels to collect energy for the building.

ML can be used to model energy consumption and in modern buildings with sensors optimise energy usage.

Energy efficiency also comes with major cost savings for occupants as well as the environment.  

The challenge is that you cannot replace building stock very quickly. Buildings also vary widely in size, shape and usage so one size fit solutions will not work. This means that these solutions take a long time to implement.

Climate Forecasting and Modelling

The many satellites orbiting the world are constantly producing huge amounts of data about land use, weather and climate patterns of the world. This data can used by scientists to build ever more complex climate models. ML has been utilised to classify crop cover, pollutants and many other kinds of data.

Deep neural networks could be used to account for cloud cover, a major source of uncertainty in climate models. Clouds can block sunlight and trap heat but are difficult to account for in climate models. Deep neural networks (an extension of ML) can be used to simulate cloud behaviour which and learn over time to improve the accuracy. Putting more accurate cloud behaviour into climate models should make them more reliable.

ML can be used to make climate predictions at a local level. Knowing which areas are likely to flood or suffer more wildfires is valuable information for firms trying to map the climate risks to their assets.

For example, an agribusiness dependent on the land will be impacted by any shifts in long term rainfall, while flooding and extreme weather could also drastically alter the productivity of the land. Understanding and modelling future shifts in climate at a very granular level will help businesses adapt.

Forests and Farmland

Huge amounts of carbon cycle through the biomass of trees, soil, bogs and peatland. Thanks to unsustainable farming practices and deforestation along with intensive cattle and livestock farming around a quarter of all greenhouse gases are released through agriculture.

As the world heats tinder dry summers cause ever bigger forest fires and permafrost which release ever more greenhouse gases. Carbon release in agriculture is not only a major contributor to greenhouse emissions but also one of the most difficult to tackle given well established agricultural practices and growing demand for meat in much of the world.

ML along with satellite imagery can identify how much carbon is released from the ground and how much is held within forests and soil. This would make is easier to identify how to manage land to where regulations are being breached and to help governments avoid further carbon release.

ML can aid reforestation by ensuring that trees are planted efficiently – by locating planting sites and then analysing data about tree health and biodiversity. ML could also be utilised to predict the direction and speed of fires, allowing firefighters to decide where to fight and where to try and stop the spread of the fire.

Adaptation to a new Climate

A new era of climate change or Anthropocene will be one of rapid adaptation. Humans will have to quickly adapt to a new world of painful extremes. Navigating a world of devastating natural disasters, disappearing coastlines and blistering heat will mean a wholesale shift in how societies function. Current infrastructure and agricultural systems will buckle under the stresses of these changes and new ways of working and living will have to be rapidly improvised.  

ML will be a critical part of mapping and planning this new world. ML has applications from predicting disasters, to designing and maintaining new infrastructure. From monitoring ecosystems to detecting carbon release from soils and forests.

ML is not a replacement for the political and economic shifts that are required for a zero-carbon economy, but they represent a potent tool which can aid the policy decisions which drive reductions in carbon emissions.

ML could also be used to measure and coordinate individuals and groups to push for action on decarbonisation. By identifying and predicting how individuals or groups will react to changes like carbon taxes, governments can assess their impact prior to implementation. This could help avoid protests and strikes that could hold up meaningful climate action.

Speculative Technologies

ML could also assist some of the speculative technologies which have been proposed to tackle global heating. Carbon capture and storage would collect C02 direct from the air and store it in the ground. The technology has been trialled, but it is extremely costly and unproven at the scale required to make any difference. ML could help model and detect where the prime underground storage places for carbon might be.

Solar geoengineering is the idea that reducing solar radiation would cut temperatures on Earth and the impact of global heating. Proposals include cloud whitening, robot boats crossing the ocean, mirrors in space reflecting the sun. All of these proposals are speculative but as desperation about climate change grows the incentive and temptation to try these moonshot ideas will grow.

Machine Learning is far from a solution or silver bullet for global heating. Instead it represents a potent tool which can be deployed in many different approaches that can aid in the battle to mitigate and adapt to the climate era.

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