Turn insights into answers with Geospatial AI
Geospatial AI integrates artificial intelligence into geospatial technologies, enabling advanced analysis of aerial imagery to highlight real-time insights and solve real-world problems.
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Artificial intelligence (AI) is trending across all software providers. But what is it? A content generator? A prediction producer? What about a data analyser? The truth is AI can be a lot of things. While this seems complex, the tool itself is essentially simple to understand.
At its core, AI is a computer system that’s capable of handling tasks that would otherwise require human intervention. This means helping with things like problem-solving, decision-making, or creative work. But did you know that AI can also be used by professionals within industries like insurance and government to improve not only their efficiencies but overall effectiveness?
Geospatial AI, the fusion of AI-driven detections and geospatial workflows, enables professionals to derive insights from robust raw data. It does this by analysing ground truth to tailor solutions to use cases, like building footprint change detection, pavement management strategies, or surface permeability calculation. Geospatial AI takes these use cases and leverages AI and geospatial commands to turn real-time insights into real-world answers.
What is Geospatial AI?
Geospatial AI combines artificial intelligence with geospatial data to create solutions that extend beyond traditional systems. This upgrade comes from processing high-resolution aerial imagery and other spatial datasets using machine learning and deep learning algorithms. When properly integrated, geospatial AI can automatically identify, classify, and analyse features like buildings, roads, vegetation, or solar panels — with unbeatable precision.
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Before AI, professionals had to manually digitise everything. This was a process that required thousands of hours to complete. Geospatial AI can do this automatically, synthesising massive amounts of data to output accurate feature identifications. But, the tool can do more than just data processing; it can enable smarter decision-making and operational excellence. To meet this potential, geospatial AI needs to be fueled by location intelligence (a combination of aerial imagery, 3D models, and geospatial tools) to train its models.
AI and aerial imagery
Aerial imagery does more than provide a top-down view of the world — it offers professionals verifiable ground truth. However, the imagery’s value is only realised if it is frequently captured at high resolution. As the imagery is updated, a historic data library is created that can be accessed and analysed by machine learning and deep learning to extract insights.
The use cases of aerial imagery for geospatial AI are many. Here are some of the more common:
Data processing and integration
Geospatial data comes from diverse sources—aerial imagery, GPS, IoT sensors, and social media. AI-powered systems can ingest, clean, and integrate massive amounts of data in real time, ensuring higher accuracy and reducing manual intervention.Predictive analytics
AI-driven predictive modeling leverages aerial data to analyse past and present imagery and anticipate trends, like traffic congestion, climate patterns, and consumer movement.Computer vision for geospatial analysis
Aerial imagery analysis, powered by AI-based computer vision, can detect urban expansion, deforestation, or infrastructure development. These insights are invaluable for urban planners and environmental agencies.Enhanced decision-making with AI-powered GIS
Geographic Information Systems (GIS) are now integrating AI to provide dynamic and interactive mapping solutions. AI-enhanced GIS platforms layer aerial imagery with real-time insights for disaster management, helping emergency responders deploy resources efficiently.
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Get ahead of peril events with AI-powered risk insights.
Geospatial AI use cases
Professionals leverage geospatial AI for challenges that require smart solutions. As AI searches for patterns in imagery and data, it surfaces insights that highlight the right time and place to take action — at a scale that’s impossible for human detection.
Geospatial AI also analyses current and historical location intelligence to power accurate predictions that can mitigate risk, support equitable taxation, select the best site for development, respond to disasters, and more.
Risk mitigation
With access to real-time data and predictive modeling, insurers can accurately assess risks associated with different locations and properties. This allows them to predict and prevent losses as the solution highlights transparent peril scores, helping underwriters and loss control teams to proactively communicate mitigation steps to policyholders.Support equitable taxation
Using geospatial AI helps assessors to accurately determine the value of properties based on location and characteristics. This eliminates potential biases or human error in assessing property values, ensuring that taxes are applied fairly and consistently across different properties. Geospatial AI also allows for real-time monitoring of changes in property values, enabling assessors to make adjustments as needed to maintain fairness in taxation.Development site selection
Geospatial AI allows AEC firms to accurately assess different sites based on factors such as topography, land use, and accessibility. This information equips them with the data to pick the most suitable location for a project — while also considering environmental and community impact. Further, Geospatial AI enables AEC firms to identify potential risks associated with a particular site, such as natural hazards or zoning restrictions.
Disaster response and climate monitoring
AI-powered geospatial analysis aids in early warning systems for natural disasters like hurricanes, floods, and wildfires. By analysing aerial imagery and climate data, government and insurance professionals can take proactive measures to mitigate damage and protect communities.
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Customer story
Bexar Metro 911
To help offsite dispatchers prepare emergency response teams arriving onsite, Bexar County required a solution that provided proactive capture, high-quality location data, and provisioning — all in one application. Nearmap AI building footprints and aerial content create shells of buildings and provide first responders with entry points, building characteristics, and more. So Bexar’s first responders now have the information they need to respond to emergency calls effectively.
Nearmap AI and geospatial data
AI depends on both data volume and data quality. At Nearmap, we control the imagery pipeline, from capture to publish. And the data is consistently high-resolution and frequently updated — captured up to 3x per year using proprietary imaging technology. This frequency and quality are ideal for driving machine learning.
“Access to up-to-date and historical imagery, along with AI data packs, has been a game-changer for us — and our clients.”
Michael Cousins, Partner, OHM AdvisorsNearmap trains our AI models using leading geospatial data before leveraging machine learning and computer vision to extract features. These tools identify raster AI in the imagery, which is used to create vector AI data. This process is run automatically after each new capture and the results are immediately available.
Picture this: 142 million high-frequency images and 13 years of modeling efforts powering advanced artificial intelligence capable of 130+ feature detections — automatically, at scale. This is Nearmap AI and it has the power to match real-time answers to real-world problems.
Ready to experience geospatial AI? Contact us today.
