Understanding deep learning in GIS
Jan 2025
Jan 2025
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Geospatial data is vital for organizations across a range of industries, from urban planning to insurance underwriting and disaster relief. With deep learning, we can now analyze that data faster and more accurately, unlocking powerful insights that drive intelligent decisions.
Whether you’re managing infrastructure projects or processing insurance claims, deep learning in GIS is changing the way we interact with the world around us. But what exactly is deep learning in GIS, and how can it benefit your organization? Let’s take a closer look.
What is spatial data in deep learning?
Geospatial data is any information about a location on Earth, like aerial maps, datasets, and even street addresses. It’s used for spatial analysis tasks from city planning to tracking environmental changes.
Traditionally, analyzing spatial data meant manually interpreting aerial imagery. Deep learning can process this data automatically and with greater precision, spotting patterns and trends that humans might miss.
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What is deep learning in GIS?
In simple terms, deep learning in GIS is the use of AI (specifically, neural networks) to analyze geospatial data. These deep learning models are designed to mimic the human brain, learning from massive datasets to recognize objects and trends in things like aerial imagery.
For example, Nearmap uses high-resolution aerial images combined with deep learning to automatically detect features like buildings, vegetation, or roads. This allows organizations to reveal location features in seconds, track how these change over time, and make more informed decisions.
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AI Layers
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Med & High Vegetation
Deep learning for mapping
Mapping is one area where deep learning in GIS adds immense value. Creating maps with traditional methods required a lot of manual work, but now, with deep learning, much of that effort can be automated. Artificial intelligence can identify and label different features — like roads, buildings, and even land cover classification — through advanced object detection, making the mapping process faster and more accurate.
At Nearmap, we pair deep learning with high-resolution aerial imagery to help industries make smarter decisions. Whether tracking urban growth or managing infrastructure, AI-powered mapping saves time and increases accuracy, allowing you to make better decisions faster.
Applications of deep learning in GIS
Deep learning’s real-world applications are already making an impact across several industries:
- Urban planning: Deep learning can help city planners understand land use, track development, and make more informed decisions about where to build next.
- Environmental monitoring: By analyzing changes in ecosystems, deep learning can help spot reductions in vegetation or even detect environmental hazards.
- Disaster response: After a disaster, deep learning algorithms can analyze aerial images to assess damage and identify risk factors like debris quickly and safely, helping responders act faster.
- Utilities management: Utility companies can use deep learning to monitor infrastructure like buildings, power lines, and pipelines, spotting potential problems before they become major issues.
- Insurance: For insurers, deep learning can speed up damage assessments, making it easier to process claims and evaluate risks using accurate, AI-powered imagery.
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Why Nearmap for deep learning in GIS?
Nearmap combines deep learning with GIS to help businesses and governments make smarter decisions. With AI-powered tools that detect over 130 property features — like roof conditions and vegetation — and high-resolution aerial imagery covering up to 87% of the population, you’ll always have the latest data. Our solutions integrate seamlessly with platforms like ArcGIS, allowing you to analyze and visualize spatial data efficiently. Whether you’re in construction, utilities, or insurance, Nearmap gives you the insights to work smarter.