While newer to the geospatial data industry, AI enables professionals to complete their jobs more efficiently and accurately. Originally, analysts required weeks to evaluate and parse massive amounts of data. Surveyors had to visit remote areas in person. Now, however, the use of AI has exponentially decreased how long it takes these professionals to do their jobs.

An AI-driven approach to geospatial analytics processes vast quantities of data and distills it into understandable takeaways, identifying trends and outliers to support a subject matter expert or decision-maker’s interpretation. Asset owners, like pipeline operators, have a much clearer and closer-than-ever-to-real-time understanding of geological threats to their infrastructure.

The Data Problem and Solution

With climate-related risks ever-increasing, it’s more important than ever to understand and mitigate asset risks. The adoption of AI-driven geospatial analytics as a source of digital transformation has grown to help organizations detect, prioritize, and monitor external threats. In fact, 36% of large and mid-sized organizations will deploy spatial intelligence software by this year, a 10% increase from 2019.

Innovative analytics leverage the power of high-performance computing, machine learning, and environmental sciences to streamline the remote sensing value chain. Remote sensing is the process of detecting and monitoring an area’s physical characteristics by measuring the reflected and emitted radiation from a safer distance. For example, rangers using remotely sensed images to track large forest fires can see a much larger area than they would from the ground.

Traditionally, remote sensing requires highly specialized talent to draw insight and guide decisions. It’s also expensive, time-consuming to process, and difficult to manage because of the multiple steps involved. AI-driven geospatial analytics brings creativity, scalability, and meaning to remotely sensed data through specialized software systems. The consolidation leads to lower overall costs for a company and higher value. 

The Benefits of AI-Driven Geospatial Analytics

The vast majority of business decision-makers believe AI holds the potential to deliver more sustainable business practices beneficial to their organizations and the communities they serve. And the global GIS market is growing. It’s expected to reach $13.6 billion by 2027, up from $6.4 billion last year. 

AI-driven geospatial analytics removes menial tasks from traditional workflows. The technology predicts, identifies, and ranks geological threats such as wildfires, floods, hurricanes, and earthquakes. This information enables analysts to determine the consequences of a potential disaster and strategize plans for various scenarios.

Analyzing and using geospatial data to proactively mitigate geological hazards saves organizations money – it’s more effective than taking a reactive approach, especially as global weather shifts. For instance, some areas around the globe not prone to flooding in the past have recently faced historic precipitation events. Geospatial analytics uses soil, climate, and other variables to uncover areas of a landscape that have become more vulnerable to accelerated geological processes like damaging erosion. 

This AI solution includes a safety component, too, facilitating decision-making when time matters. For example, it allows employees to assess dangerous situations – like a landslide near a pipeline – remotely, rather than requiring a boots-on-the-ground evaluation. Someone monitoring the area can use LiDAR to extract near-real-time data about vegetation, the landslide’s direct impact on the pipeline, and the surrounding environment.

An AI-driven approach to analytics overhauls the geospatial industry by helping workers complete their jobs effectively, accurately, safely, and, more and more often, remotely – providing a catalyst for the broad transformation of individual organizations.