"The scale and complexity of the bushfires and weather systems was unprecedented — it pushed people, processes and existing technology to their limits."
During the devastating Australian bushfires in the summer of 2019/2020, fires burnt more than 18 million hectares of land. More than 30 lives were lost, billions of animals were killed, and damage estimates exceeded $200 billion.
Kablamo took on the challenge of rethinking how data and machine learning could be used to better manage, predict, and prevent bushfires in the future.
The challenge
From interviewing volunteer firefighters, brigade captains, fire-behavioural analysts, fire scientists, and technology teams, we uncovered critical opportunities:
- Physical servers were unable to cope with incoming data volume
- Manual analysis was creating dangerous delays
- Disconnected systems across agencies hindered coordination
- Almost limitless data (satellite imagery, wind, social media) needed processing
The approach
A scalable cloud data platform delivering bushfire prediction models via an intuitive user interface.
We formed a specific project scope: to build a robust, scalable cloud data platform that delivers bushfire prediction models via an intuitive and interactive user interface.
Our designers composed an accessible user interface that displayed prediction knowledge and management advice with accurate maps, enabling fast communication between teams during times of high pressure:
- Centralised AWS cloud data platform for quick and reliable analysis at scale
- Future-ready architecture for AI and ML components
- APIs for prediction models and mapping systems
- Accessible UI designed for high-pressure decisions
Results
Within a matter of weeks, Kablamo had created a working prototype providing a scalable cloud platform and intuitive user interface for displaying real bushfire data and managing predictions.
Looking forward
The new data platform assists State and Territory firefighting services to be more prepared for longer, more intense fire seasons. The scalable platform enables more sophisticated data analysis, machine-learning-based fire prediction models, and automated fire response mechanisms including robotics and drones.
