How AI is Changing the GIS Landscape

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Artificial intelligence (AI) has grown exponentially in recent years. It’s able to match and, in some cases, exceed human accuracy at such tasks as reading comprehension, image recognition, and text translation. However, an area that is seeing massive opportunities that weren’t possible previously is GIS (geographic information systems). GIS is a computer system that displays and analyzes geographically referenced data.

In broad terms, AI is the capacity for computers to perform tasks that usually require some degree of human intelligence. Machine learning is an approach that can perform this method. It utilizes algorithms to acquire information from the data to provide the necessary answers. For instance, machine learning can help with automated territory map generation.

Although machine learning has been a crucial part of GIS software in Clustering, Classification, and Geographically Weighted Regression, spatial analysis can now go further by using deep learning tools. Let’s look at some cases of deep learning’s application in geographically referenced information.

Deep Learning’s Application in Geographically Referenced Information

  • Image Classification

Deep Learning can be used to determine whether a photo is type A or B so as to categorize geotagged photos.

  • Instance Segmentation

Instance segmentation is a more exact Object Detection method from which the precise shape of an object in an image can be derived. Using this method, GIS can be combined with LiDAR data to recreate buildings in 3D.

  • Semantic Segmentation

This process classifies each image pixel, so it belongs to a specific class. In GIS, this approach can be used for Land Cover Classification.

  • Object Detection

Object detection is a computational approach that finds objects within an image by coding and locating them. In GIS, combining this process with aerial photography, satellite imaging, or drone photography makes it possible to map objects of interest.

Machine Learning and Location Intelligence for Real-World Applications of GIS 

Using location intelligence, GIS technology, and Machine Learning automation, industries are becoming more innovative and gaining real-time insight. By combining these methods, businesses are gaining the ability to map, analyze, and share data in the context of location. For instance, they can spot trends and make predictions to support market assessments, site selection, asset tracking, risk management, and various other central business needs. Simply put, machine learning manages complex data, and location intelligence provides the data with crucial location context. 

Let’s look at some real-world examples of how these tools are being applied in various industries.

  • Retail Industry

In the retail industry, machine learning and location intelligence have many applications. Retailers can use these tools for site selection, optimizing their supply chain, and location-based advertising. These tools can also help with customer support, providing personalized customer experiences, and setting prices.

  • Government Agencies

Government agencies apply machine learning algorithms on georeferenced satellite and drone imagery. This allows them to automate model growth scenarios and fieldwork, predict crop yields and assess the health of crops in real-time.

  • Logistics

Drivers, route planners, and operations managers can use AI to track assets in real-time, anticipate future supply needs, accurately predict arrival times, and fill in the gaps in road network databases.

  • Finance

Machine learning helps banks and financial analysts detect fraud, perform predictive risk assessments, and plan either one branch location or a network of multiple locations.about:blank

  • Manufacturing

When it comes to the manufacturing industry, manufacturers can use AI systems to automate inspections and quality control, optimize supply chain logistics, plan predictive maintenance, and flag any unusual activities that can slow production.


Artificial Intelligence is changing the GIS landscape by using deep machine learning to improve the analysis of geographically referenced information. AI, specifically machine learning and location intelligence, is also being used to help various industries analyze and improve their processes.

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