The maps that planners, insurers, and governments rely on to understand the physical world don't update themselves — someone has to build the systems that turn raw aerial imagery into ground truth. That's this team.

This role is all about translating R&D from other parts of the Nearmap AI & Computer Vision team into data and ultimately, answers. You will be a core contributor to the algorithmic systems for creating conflated Map Data

products.

Conflation is the process of combining multiple data observations about the world into a single, cohesive map that prioritises usability, practicality, and a straightforward representation of real world objects. Input data sources include aerial imagery (multiple surveys over time, 2D, 3D, multi-angle, and captured by multiple providers), other geospatial sources such as property data, permit data or suitably licensed open data sets. A key challenge is to provid...