What is machine learning in GIS?
Dec 2024
Dec 2024
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In our fast-changing world, machine learning in Geographic Information Systems (GIS) is reshaping how industries operate, from construction and engineering to government and insurance. By combining machine learning with geospatial data, organisations can gain fresh insights that inform smarter decisions.
Explore what machine learning means for spatial analysis, learn its benefits, and examine how Nearmap is leveraging these technologies to drive innovation in this dynamic field.
What is machine learning for geospatial analysis?
Machine learning for geospatial data refers to the application of algorithms that enable computers to learn from and make predictions based on geospatial information. By integrating machine learning into GIS, organisations can enhance their spatial analysis capabilities, enabling more informed decision-making.
Machine learning models thrive on diverse spatial data, such as terrain maps, population density statistics, and environmental conditions. This approach provides the foundation for valuable new applications, from urban planning to insurance underwriting.
At Nearmap, we’ve integrated artificial intelligence (AI) and machine learning into our aerial imagery processes, enabling us to deliver insights at an impressive scale. According to Dr Michael Bewley, Senior Director of AI Systems at Nearmap, “We do AI at the same scale that we do imagery.” With each aerial survey, our customers gain access to over 150 AI-derived insights about specific locations — covering everything from vegetation to construction sites.
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AI Layers
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What are the advantages of machine learning in GIS?
The combination of GIS and machine learning comes with some fantastic benefits for organisations:
- Enhanced accuracy: One of the standout benefits is improved accuracy. Machine learning algorithms minimise human error by leveraging large datasets, allowing for insights that manual methods might miss. Nearmap AI models boast incredibly high accuracy, delivering results that are hard to achieve through traditional labelling methods.
- Increased efficiency: By automating data processing, we can analyse billions of features across vast areas simultaneously, which means faster insights and time saved for our customers.
- Predictive capabilities: With machine learning, GIS can help anticipate future trends based on historical data. This is invaluable for sectors like urban planning, where proactive decision-making is crucial.
- Scalability: The power of machine learning means we can handle large datasets with ease. This scalability is essential for comprehensive geospatial analysis, particularly in complex fields like environmental science and urban development.
Applications of machine learning in GIS
Machine learning is already making waves in various applications, including:
- Urban planning: By analysing factors like population density and land use, machine learning can help urban planners make informed decisions about resource allocation and development.
- Environmental monitoring: By processing environmental data, machine learning uses image segmentation techniques to help track changes in land use, monitor deforestation, and assess climate change impacts.
- Transportation and logistics: Optimising route planning and analysing traffic patterns are just a couple of ways machine learning is streamlining transportation and logistics.
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The Nearmap approach to AI and machine learning
We’ve developed a seamless system that combines aerial imagery with AI and machine learning to deliver enhanced insights to our customers. With our latest updates, users can access more than 130 data layers organised into AI Packs, providing valuable, scaleable geospatial intelligence on a wide range of features — from roof conditions to surface types.
Gain fresh insights that inform smarter decisions. Get in touch with our team of experts today to learn how machine learning in GIS could transform your workflows.