Artificial intelligence and big data analytics are becoming increasingly relevant and important to GIS users. This post will explore the convergence of GIS, AI, and big data and the exciting possibilities it brings for the decision makers.

Artificial intelligence and big data analytics have become a buzzword in the geospatial industry over the past few years. Spatial data is increasingly being analyzed in a variety of new ways, based on machine learning algorithms. What was once interpreted as an entry-level task in GIS is now reaching a new level of sophistication.

At the same time, the advances in big data can empower GIS professionals to better extract new insights from the spatial data they collect.
With big data comes a wealth of potential data, both in terms of size and quantity. However, getting access to this data can be a cumbersome and time consuming process for many organizations. 

There are a number of ways in which artificial intelligence and big data can improve GIS. Big data is a necessary prerequisite for the future development of GIS because it will increase the amount of spatial information that GIS users can be presented with.
The article discusses various reasons that are prompting organizations to consider the integration of AI and big data with GIS.

Advantages of integrating AI, Big Data and GIS

Artificial intelligence and big data have become widely recognized solutions for optimizing GIS processes.  Here are the major benefits the convergence can bring to the domain of spatial analysis.

  • Business intelligence – GIS and big data are used together in the financial services sector to solve more complex problems. For example, it can be used by banks to decide which branches to merge or by insurance companies to identify potential fraud. Financial services companies, telecommunications companies, and governments use business intelligence and GIS tools for making informed decisions. Many financial startups are using satellite imagery to determine the potential risks of providing insurance or credit. 
  • Marketing – One of the most common uses of geoAI and big data in marketing is in customer segmentation. For example, sports equipment companies can use fitness tracker data to segment audiences based on physical activity and provide timely promotional information. Organizations can use social media to track brand awareness in selected regions. This can help them to reach customers with more targeted messaging. 
  • Humanitarian projects – Technological advancements, including the Internet of Things, artificial intelligence, and big data have provided an incredible amount of information that can help countries achieve sustainable development goals. Organizations can use satellite data, combine it with other sources such as social media sentiment and aerial imagery, and further use GIS machine learning algorithms to track activity in specific locations for detecting anomalies. This can help to solve a range of humanitarian issues from global poverty to child trafficking and disaster relief. 


Artificial intelligence and big data analytics are the future of GIS. These technologies will change the way we analyze and interpret spatial data. It is fining increasing applications in a number of fields such as marketing, business intelligence, location analytics, disease surveillance, climate modeling and analysis, disaster response, and banking. The potential of big data and artificial intelligence (AI) in GIS is growing rapidly, enabling a new wave of GIS data analytics.  

SBL is an industry leader in offering cutting-edge GIS solutions that integrate the latest technological developments such as IoT, AI, and big data with GIS. We can help your organization the full potential of spatial intelligence with our reliable GIS solutions. Contact us today, to discuss your requirements.