GIS, Artificial Intelligence, and Automation in the Workplace

01-12-2021 09:51 AM
Esri Contributor
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Authored By: Steve Snow

Esri Senior National Government Industry Strategy Specialist

Part 2 of  a 7 part series exploring GIS and Artificial Intelligence


Many professions are being impacted by the introduction of Artificial Intelligence (AI) in the workplace. Future employment and career choices will be forever changed by how susceptible they are to automation and computerization.

One study that focuses on the impacts of AI and Automation in the Workplace is from the website Will Robots Take My Job? This website started from a report by Carl Benedikt Frey and Michael A. Osborne title “The Future of Employment: How susceptible are jobs to computerization?”. The authors estimate about 47 % of US employment is at risk due to automation by 2033.

Of concern are AI automation impacts on geospatial staff. Using the websites interface on Will Robots Take my job, it was found that there were 4 geospatial job types that are expected to be impacted by 2033. The following 4 job searches: Civil Engineers, Geographers, Cartographers and Photogrammetrists, and Surveying and Mapping Technicians delivered interesting results.

As you can see from the below results, Civil Engineers have a likely replacement of about 2% and are safe from automation. Geographers have a higher replacement risk of 25%. When we get to both Cartographers and Photogrammetrists and Surveying and Mapping Technicians the risks are very high. One possible reason for this is that there are highly repeatable workflows which lend them to the strengths of a machine such as GeoAI to automate.

The results of the below Image stress the reason why AI skill sets in the future will be more important than ever as a job skill. I will be covering more on AI and human teams in Part 3 of this series.



Example 1 Automation Today (not including GeoAI)

One example of automation (not including GeoAI) in the workplace is the Dutch Kadaster who is the National Mapping Agency of the Netherlands. The Dutch Kadaster originally was looking at ArcGIS automation for their 1:50,000 map series for the country. The series typically required 28 cartographers and 5 years to update the system manually. After implementing ArcGIS automation (non-GeoAI) the mapping agency went down to 1 cartographer with completion of series in 3 weeks of processing using a 100% automated approach with an infrastructure of 6 computers utilizing 6 CPU’s each. The Return on Investment using ArcGIS was a 5000% reduction in time and resources. You can find out more by visiting the article Transforming National Map Production.



Example 2 Automation Today (using GeoAI)

A second example of automation using GeoAI was by the AAM Group for Electric Utilities in Australia. The basic problem was how to automate the manual classification of Lidar Point clouds. GeoAI was used to automate the Lidar workflow over a large geographic region. After creating the GeoAI workflow in ArcGIS the Return on Investment resulted in 50,000 man hours and 5 million dollars saved on the project! One of the added benefits of using GeoAI with ArcGIS was that the buildings and vegetation were also classified by the AI. Additionally, further benefits were that the GeoAI LiDAR model is now being applied to other projects. You can read more be reading the article PointCNN Replacing 50-000 man hours with AI.



GeoAI in Dynamic Environments

An area that GeoAI will be a future asset to geospatial organizations will be in the areas of navigation, sea level rise, disasters, and predicting coastal flooding during storms. The human and AI team can quickly perform feature extraction from littoral and coastal areas where there is constant and rapid change. Extraction of shoreline and the land/water interface is important for many federal, state, and local government agencies. Currently much of this is still a manual process, but a human and AI team could rapidly update products to map and provide authoritative information to rapidly changing environment and situations.



Read Part 3 of 7: Teaming with the Machine – AI in the Workplace


Additional Blogs in the Series: 

Part 1: Future Impacts by AI on Mapping and Modernization