When Geospatial Intelligence meets AI

Artificial Intelligence (AI), in the past few years, has steadily moved on from theory to mainstream practice, ushering an era of digital transformation. AI today allows us to execute tasks that weren’t possible a decade ago, allowing businesses to simplify complex processes and scale their operations by gaining access to rich data and insights that weren’t available previously. Integrating AI with the Geographical Information System (GIS) facilitates faster processing of information and generation of far better accurate insights and geospatial intelligence, this far-reaching and impactful confluence is called GeoAI. 

Why is GeoAI critical for the future of geospatial intelligence?

Using AI in GIS presents a unique opportunity of combining spatial analysis of huge datasets with an almost human-level processing accuracy. We can combine AI with geospatial applications in the following ways:

Remote sensing for Earth observation: Segmenting, classifying, down-scaling, or fusing ground-level or satellite imagery.

Remote Sensing by a satellite

Spatial data analysis: Interpolation of geospatial data with generative adversarial networks.
Geographical text analysis: Address geo-coding or geo-referencing place references in documents.

We look at aerial imagery, we look at auto change detection, classifying the features; we are comparing those features automatically with the previous versions of the map. Therefore we are able to spot the changes that have occurred. That is a form of machine learning and artificial intelligence. We sometimes call geospatial a golden thread which links so many datasets. It is going to be at the heart of making sense of the trillions of bits of data.

Nigel Clifford, CEO, Ordnance Survey, United Kingdom.

What are the benefits of combining geospatial science with AI

The three major advantages of combining geospatial science with AI are as follows.

Multiple data sources handled

Multiple data sources handled

The most valuable geospatial intelligence data always have one thing in common– researchers derive them from a combination of multiple sources of data. Some data sources can balance the weakness/power of others to provide comprehensive results. The outcome will always be better than using individual data sources.

Processing incredibly high volume of data

Processing incredibly high volume of data

There is an enormous volume of geospatial intelligence data that the geospatial sensors in the world are collecting. Mining this mountain of data to find actionable insights is time-consuming and inaccurate via manual methods. The use of artificial intelligence and machine learning definitely helps speed up the process to generate efficient results. More often than not, a human intelligence layer is preferred as the final endpoint before using the data but the effort required is much less. Therefore, GeoAI makes the human analyst effective.

Correlating a large number of parameters

Correlating a large number of parameters

When you use multiple variables to measure the correlation between different factors, the results are not comprehensive. By making use of machine learning,  we can establish a correlation between random variables in a data set. This is a very useful aspect of AI for geospatial datasets as the number of parameters present in such datasets is very large.

Scope of geospatial intelligence and AI

Scope of Applications

The combination of artificial intelligence (AI) and geographic information systems (GIS), creates geospatial artificial intelligence (GeoAI). More than just ride-hailing services like Uber, GeoAI is providing critical information to national labs, defense agencies, insurance companies, weather centers, agriculture, and many more.

AI is there to help the human, it’s not there to govern and provide the answers–it’s there to augment.

Matt Jones, an analyst at Tessella

How is it being used – Case Studies

While GeoAI finds its applications in several fields, here are two prominent examples of its use.

1. Predicting traffic patterns and preventing traffic accidents

Traffic snarls are always a problem, and the situation only worsens if there is an event where thousands of people turn up. A county in the United States is making use of AI, IoT, and location technology to combat traffic snarls. The county has partnered with a stadium owner in a large metropolitan area to observe street cameras and adjust traffic signals to regulate vehicle and pedestrian movements. They are making use of trend detection, which is an advanced machine learning technique. The results are much better as compared to pattern recognition techniques that can, at times, detect road signs in static images. The machine learning algorithm here analyzes the videos of the departing crowd from the stadium in real-time. As crowds and traffic build, they adjust traffic lights to ease congestion. By making adjustments at certain locations, it prevents the entire system from slowdowns.

2. Reducing the time to market for high-definition maps

Mapping and location data services provider HERE Technologies wanted precise digital maps for autonomous driving applications. Attentive AI made use of deep learning technology to create a comprehensive solution for HERE Technologies that automated their digital map creation to a large extent. As a result of automating the process, HERE Technologies team got the chance to focus more on ingesting the map features into their base products and services. Because of reduced turnaround time and improved output, they were able to deliver products quicker than their competitors.

3. Use of Geo-AI for COVID-19

With the combination of geospatial intelligence and AI, many datasets and platforms have been developed to spread awareness of COVID-19 as well as to provide spread and infection analytics to governments, health organizations, and research facilities.  This has been a major contribution to fighting this pandemic. 

The future impact of GeoAI

Location is an important factor that helps researchers gain relevant insights and find solutions to overcome challenges. Insurance, transportation, energy, and several other public sector organizations need data to meet their business objectives. However, more than just data, they need quality insights in the shortest time and handing them a strategic tool that will revolutionize the way they do business and achieve their objectives. This is just the beginning of the GeoAI era.

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2 Responses

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