The Rise of AI-powered Geospatial Analytics

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In Utah, a Machine Learning enabled Geospatial AI algorithm can predict car accident hotspots on segments of the road network. 

In New York, an AI-powered algorithm helps trash collectors map out the best route depending on how full the trash cans are. 

Google Flu Trends uses AI to identify flu hotspots in the U.S quickly. Similarly, there are hundreds of examples where spatial analysis techniques are aiding society and people to save lives and make the world a better place to live.

There are two primary components at work in the above examples – The inputs are from geospatial mapping or remote sensing. Satellite imagery portrays the ground reality without actually requiring physical presence. It is accurate at high-resolution, available at the needed level of frequency, and has become relatively inexpensive in recent times. 

The AI model is the second component. Broadly, there are three ways in which AI has been used in conjunction with geospatial analysis.

The first is for land classification. By training your algorithm to identify types of terrain, it can identify land-use patterns.

The second is clustering, where you can group similar data points.

The third is for prediction. Using algorithms like spatial regression, you can model future outcomes and relationships. 

With these algorithms, our data scientists have created multiple use cases on spatial data science technology. They are helping organizations to fight deadly diseases with Geospatial AI, build resilient cities, and conserve biodiversity with satellite imagery.

Know how Gramener is building cutting-edge GeoSpatial AI solutions with companies like Microsoft, Evergreen Canada, and World Mosquito Program. Check out our Spatial Analytics solutions and offerings.

Why Geospatial Analysis is Merged with AI?

What we have described above is called Geospatial Artificial Intelligence, an emerging interdisciplinary concept that combines Geospatial Analysis and advances in Artificial Intelligence.

Leveraging the power of supercomputing, Geospatial AI can extract and impart impactful insights with inputs from satellite imagery and remote sensing.

According to an article in Environmental Health, 80% of all data are geographic in nature, as the majority of information around us can be georeferenced.

By applying Data Science techniques to this data, we can glean valuable insights like enabling smart farming, understanding crop yields according to geography, or shifts in average temperature in a particular region.

Here are some of the most significant advantages of the approach combining spatial data and AI: 

• AI saves hours of manual effort required to analyze GIS data. By automating hundreds or thousands of repetitive tasks like classification or clustering, AI has the ability to minimize turnaround time. As the solutions are AI-powered, they are precise and eliminate human error
• AI algorithms have the ability to factor in new geographic data as and when it is received, “learn” from these patterns, and generate suitable outputs
• They are scalable and can run 24×7.

What has Driven the Growth of Geospatial AI?

There are a few critical technical factors that have driven the rapid adoption of Geospatial analysis in the field of AI. These developments make this technology robust and ensure accelerated growth in the coming years too. 

Availability of Data

Data quality is usually measured by the five Big “V”s. Due to advances in satellite imagery infrastructure, all the Vs are in place.  

• Volume: The number of satellites orbiting the earth is set to quintuple in the next decade. This means that there is more geospatial data available from satellites. The better quality of data is available due to better technological capabilities and a much lower cost (in many cases, free of charge/open source).
• Velocity: As more satellites orbit the earth at different times, the more data is available quicker.
• Variety: Satellites provide a wealth of information, including images (optical), temperature and weather data, and SAR data (Radar).
• Veracity: As the data is directly captured and not manipulated, they are a source of truth.
• Value: Nearly every organization or government can use satellite imagery to optimize supply chains, identify where their customers are, or track health and other trends.

More Computing Power, Cloud Storage

Geospatial AI generally requires high computing power due to the volume of imagery involved in satellite data.

According to this article, a recent research report found that an approximate minimum computation requirement for training a deep neural network on a dataset of 1.28 million images would be on the order of an ExaFLOP.

At a supercomputing level, in 1993, supercomputers could perform 124.5 GigaFLOPS. In 2020, this number stands at 415.53 PetaFLOPS. 

This means that Geospatial AI algorithms can run faster and more efficiently on today’s computers.

Another advance is that of cloud computing. As satellite imagery requires enormous amounts of storage space, the cloud is the most logical solution to host data and even the programs that manipulate it. 

Gartner forecasts that globally, public cloud revenue is set to grow 17% in 2020, becoming a $350 billion industry by 2022. According to data cited in a  Forbes article, 83% of enterprise workloads will be on the cloud by 2020. 

Better Algorithms with Geospatial Analysis

According to a McKinsey study of over 400 use cases in 19 industries, AI provides better results than other analysis techniques in 69% of the cases. In 16% of the cases, AI is necessary to capture value. 

Advances in computer vision have made it possible to get credible intelligence from Satellite Imagery using Artificial Intelligence techniques such as Deep Learning. 

The increased computing capacity has ensured that more efficient and complex AI algorithms can be run on massive Geospatial datasets.

Satellite Imagery is Domain-agnostic

This means that any problem can be converted into a geospatial one and answered through Geospatial AI. Satellite data can be applied to nearly every possible domain, from monitoring infrastructure to healthcare and crime rate.

World Class Map Visualizations

Yes, data visualizations play a huge role in showcasing insights. In the Geospatial world visuals such as dot density, choropleth, cartogram, and many are used to plot GIS and satellite imagery data on a map. Check out our article on spatial data visualizations to know how it is aiding urban city planning and building resilience.

How Can You Set up a Geospatial AI Solution?

At Gramener, we specialize in geospatial analysis and AI to solve enterprise problems as well as pressing societal issues such as climate change.

One of the problems we frequently encounter also happens to be a prerequisite for many kinds of analysis. This is the estimation of the population in a region.

Once this is done, the next steps could be estimating crime rates or water usage per person. You can even go on to evaluate whether it makes sense for a new retail outlet or a supermarket to open in the area depending on the population size. 

Let’s look at a real-world Geospatial AI solution that Gramener has developed: 

Quality of Life with Geospatial Analysis and Satellite Imagery

Given a location name or its latitude & longitude, Gramener’s AI-powered Satellite Imagery application, Quality of Life, can identify useful demographic features such as: 

• Population density
• Quality of infrastructure
• Access to good healthcare

We trained a Deep Learning model using the following: 

• Satellite imagery, Sentinel-2 Satellite from Google Earth Engine which is available at 10m resolution
• Demographic & Health Surveys, geo-coordinated and anonymized to maintain privacy of clusters
• The model takes satellite imagery as input and predicts the features present

The results can be used as a proxy to identify the demographic features of a region. Governments and social organizations can use these insights as inputs for their decision-making.

In crisis situations, geographical intelligence can be used for outreach or disaster-management efforts.

Would you like to know how your organization can leverage the power of Geospatial Analytics to build solutions like this? Attend the first of our three-part webinar on Geospatial AI.

Contact us for custom built low code data and AI solutions for your business challenges and check out spatial analytics solutions built for our clients, including Fortune 500 companies. Book a free demo right now.