Location Intellegence
Design Vision
Company: Nearmap
Platform: Web app
Year: 2023 - 2025
Context
Nearmap has been an industry leader in geospatial technology, known for delivering high-resolution, frequently captured aerial imagery across Australia and the USA. As customer needs evolved, Nearmap began expanding its capabilities through AI-powered detections and data enrichment, transitioning from an aerial imagery provider to a location intelligence data platform. This shift required not only advances in machine learning and data infrastructure, but also a fundamental rethinking of how users discover, interpret, and act on complex geospatial insights.
Key Contributions
1. Establishing the Foundation for Computer Vision
I led the design of foundational tools and experiences that enabled Nearmap’s computer vision capabilities—from training AI models to shipping AI outputs live.
This included designing an internal machine-learning labelling tool with an end-to-end workflow that allowed human labellers to train AI models using consistent, high-quality ground-truth data. In parallel, I introduced the AI Viewer experience in MapBrowser, our flagship platform, enabling users to visualise and explore 130+ AI detections directly on aerial imagery—including swimming pools, roof and building attributes, and more.
These foundations bridged data science and user experience, making AI outputs interpretable, trustworthy, and actionable for customers.
AI viewer in MapBrowser
2. Turning AI Detections into Property-Level Insights
To move beyond raw detections, I designed PropertyVision, a new platform that shifted Nearmap’s UX from an open-ended map interface to a property-centric experience.
This project reframed AI detections into coherent, story-driven property insights tailored for P&C insurance underwriting workflows. Key features included the Roof Score AI and other imagery-derived attributes that helped underwriters quickly assess risk, eligibility, and confidence at the individual property level.
The design focused on clarity, explainability, and workflow alignment, ensuring complex AI insights could be consumed intuitively by non-technical insurance users.
3. Scaling from Property Insights to Portfolio Intelligence
Building on the property-centric foundation, I led the design evolution toward portfolio-level intelligence—enabling insurers to assess and manage risk across millions of properties.
This included designing high-level portfolio summaries, filtering and prioritisation mechanisms to identify policies requiring attention, and interaction patterns that supported concentration analysis and risk-mitigation strategies. The same design systems and patterns were extended beyond underwriting into claims workflows, including catastrophic event response, ensuring consistency across the insurance lifecycle.
By scaling insights from single assets to entire books of business, the design vision supported more strategic and proactive decision-making at enterprise scale.
Outcome
This multi-year series of projects at Nearmap established a scalable, system-driven design foundation that supports Nearmap’s evolution into a multi-platform location intelligence data warehouse. It aligned AI, UX, and business workflows to transform imagery into actionable insights, bridging property-level details and portfolio-level decision-making while remaining accessible to non-technical users.