Geospatial AI Solutions and Use Cases Explained

While many consider the technology to still be in its infancy, geospatial AI solutions have already demonstrated the ability to automate many manual processes and provide analyses far more quickly and more accurately than humans can, while using fewer resources – given the right conditions.

In this article, we will discuss geospatial AI use cases to exemplify how this technology is already gaining traction across many industries and possibly give you some ideas if you are thinking about implementing AI solutions within your organization. But before we get to that, let’s start with the basics.

What Is Geospatial AI?

In the simplest of terms, geospatial AI is the combination of spatial computing or geographic information systems (GIS) and innovative technologies like artificial intelligence and machine/deep learning.

GeoAI is an emerging subfield within machine learning where geographical data is used as the base for ML models, analyses, and predictions. Although relatively new, this type of AI already sees a wide range of uses in practice – from asset management to geographic modeling to smart logistics. But it does come with a unique set of challenges.

The Challenges of Using Geospatial AI in Practice

While geoAI has a lot of potential, its application is limited by several factors. First, acquiring high-quality geospatial data is a complex undertaking and often very expensive. Second, training geospatial ML models on large datasets requires a lot of computational power. Third, the proper application of geoAI requires specific data science expertise, which is in relatively short supply.

These limiting factors are so severe that NASA and IBM have partnered to create a foundational geospatial open-source AI model, trained on Harmonized Landsat Sentinel-2 satellite data. The foundational model is a pre-trained model that can be used as the basis for developing more specialized geospatial AI.

But even with these limiting factors, geoAI still sees a lot of use in practice – so let’s discuss that.

GeoAI Use Cases

In essence, geoAI can be beneficial to any industry, business, or project that has a need for highly accurate and automated analyses that are, at least in part, based on geospatial data. Here are just some of its uses.

Environmental Monitoring

One of the most common uses of geospatial AI is for environmental monitoring. With the input of historical geographical data, sensory data, and drone and satellite imagery, AI algorithms can be trained to track any changes in ecosystems and predict the environmental impact of those changes. 

Geospatial AI can be used to analyze vegetation indices and so make predictive models for deforestation or make crop yield predictions. It can also be used to detect greenhouse gases. These models can be trained to provide early warning for natural hazards, like wildfire risk assessments.

In short, geospatial AI may soon prove to be invaluable for environmental monitoring and management.

Urban Planning

One of the basic applications of geospatial AI is in urban planning. 3D geoAI models can provide governments with highly accurate location intelligence, including a geospatial analysis of data like population density and land use, allowing engineers and architects to plan accordingly. Geospatial AI can even go a step further, predicting urban growth and thus enabling governments to plan for future outcomes accordingly.

The Transportation and Logistics Sector

Likely unsurprisingly, geospatial AI has long found a use in the transportation sector. ML models can be taught to automatically interpret real-time spatial data and provide analyses. One of the most common examples is GPS route selection – when specific routes are chosen based on the current state of traffic in a specific location.

But as you likely know, it doesn’t always work perfectly. However, those kinds of models can be fine-tuned to perform a more accurate spatial analysis, so any logistics company that has a need for route optimization can find a use for geoAI.

Autonomous Vehicles

Self-driving cars heavily rely on geospatial AI and remote sensing. AI models are used to detect, track, and analyze objects in the vicinity of an autonomous vehicle and then geospatial AI can plan a route. The vehicle’s path is constantly updated based on the dynamic circumstances, allowing cars to drive autonomously. But, again, as you likely know, this technology has not been perfected yet.

Healthcare Resource Allocation

Another use for geospatial AI can be found in the healthcare sector. AI can be used to analyze information regarding disease outbreaks, demographics, and the distribution of healthcare facilities. This type of analysis can enable healthcare organizations to make informed business decisions regarding resource allocation and ultimately improve public health.

Preventive Infrastructure Monitoring

Geospatial artificial intelligence can also be used to support infrastructure maintenance with valuable insights regarding real-world geospatial data. GeoAI models can be trained to analyze image and video data, like drone, satellite, or LiDAR data, to detect the need for maintenance.

These models can detect structural issues in electric grids, pipelines, bridges, dams, roads, etc., informing of the need for repairs. Thus, the use of geoAI can lead to both increased safety and reduced repair costs.

Have a Specific Geospatial Project in Mind?

These were just some of the most common use cases of geospatial AI, but it has many other applications – from large to small and unique projects. Aya Data offers services across the entire AI chain. We can help with any part of a geospatial project, be it data acquisition, annotation, model implementation, or even building a bespoke geospatial AI solution.

If you have a project or even just an idea in mind, schedule a free consultation with one of our geospatial data scientists so that we can discuss how Aya Data can contribute to your project.