Akula Tech among the only two Australian Teams to qualify for the XPRIZE Wildfire challenge

X- PRIZE Wildfire is a 4-year, $11 million competition incentivizing the innovation of firefighting technologies that will end destructive wildfires so that humanity and beneficial wildfire can safely co-exist
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How is AkulaTech leveraging its advanced satellites to improve wildfire detection and monitoring?
AkulaTech is developing an advanced cloud-based platform for detecting wildfires. This platform will use data from AkulaTech's cutting-edge satellites, which are equipped with real-time machine learning for continuous, autonomous monitoring.

The data processing system will be trained using a large collection of images from past wildfires worldwide, gathered from existing Earth observation satellites. This ensures the platform’s accuracy when it’s deployed. The project is led by AkulaTech and involves collaboration with the University of South Australia, Swinburne University of Technology, SmartSat CRC & AIILA.

How does Akula Tech intend to tackle this problem?

1

We're building a robust cloud-based pipeline designed to ingest raw Earth Observation data from various sources, whether it's external satellites, existing constellations, or our upcoming in-house constellation. By capturing this data as close to the downlink as possible, we ensure immediate access and processing, setting the stage for real-time insights and analysis.

2

Next, we leverage powerful cloud instances to apply advanced data correction techniques. By performing radiometric, atmospheric, and geometric corrections, we ensure that the data aligns precisely with ground observations, guaranteeing accuracy and reliability.

3

To ensure seamless data pre-processing, we address the varied characteristics of hyperspectral and multispectral sensors—factors like spectral resolutions, ranges, profiles, and spatial resolutions. Our middleware algorithms are designed to handle these differences efficiently. They perform tasks like upscaling/downscaling spatial resolution and automating band clustering/selection, ensuring that the processed data is optimally prepared for input into our advanced algorithms and AI models.

4

We harness the power of cloud-based AI models for fire detection and inference. This cloud approach enables us to utilize extensive computational resources, ensuring our models operate with the highest levels of precision and accuracy.

5

We're developing a web-app dashboard tailored for firefighting agencies, providing them with real-time access to live observation and detection data. This intuitive platform empowers users to monitor critical information instantly, enhancing their ability to respond swiftly and effectively.

6

Our web app integrates a human-in-the-loop validation system to ensure the accuracy of cloud-based fire detection. This added layer of human oversight helps to eliminate false positives, providing greater confidence and reliability in our detection results.
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