MAWA-1 is an autonomous wildfire detection system that can detect wildfires as small as one square foot from an altitude of over 3,000 feet. The system is capable of patrolling about 4,000 hectares per hour and automatically detect, report and map hotspots that are too small to be detected by human observers.

This is ground-breaking technology and an industry-leading solution that can significantly improve the speed, accuracy and economics of forest fire detection.

Operating Cost:

MAWA-1 was designed around Next1’s EEE (Efficient, Economical & Easy to operate) principal. Thanks to its EEE design philosophy and autonomous functionality, the MAWA-1 system is extremely economical to operate.

Flyaway cost (including labour & Airborne platform) for a single MAWA-1 system is between $199 to $399 per hour, depending on system configuration.

System design

The system is fully customizable and consists of three fully independent and swappable modules:

• Airborne platform

• Sensor package

• Processing Software

Airborne platform

MAWA-1 can be installed on most high wing fixed-wing aircraft (like Cessna 172) including ultra-light and experimental aircraft without any modifications. It is also compatible with most commercial UAS available in the market today.

This makes MAWA-1 extremely flexible in terms of deployment in any regulatory and/or geographic environment.

Sensor package​

MAWA-1 uses an integrated sensor package that combines high resolution radiometric thermal images, a 4K colour camera, GPS, IMU, magnetometer and a barometer to produce the most powerful multi-sensor imaging solution available in the market today.

The sensor is capable of simultaneously recording high resolution (640 x 512 pixels) radiometric thermal images, ultra high resolution (4000 x 3000 pixels) RGB still image as well as 1080P HD video.

MAWA-1 is also compatible with numerous other sensors available in the market today.

Processing Software:

Next1 has developed proprietary software to collect and process a wide range of data from the MAWA-1 sensor package. Advanced algorithms are applied to process the data in order to find temperature anomalies. If temperature anomalies pass a certain threshold, these hotspots are flagged as possible fire threats and are autonomously reported and mapped, with the specific details of the threat sent to a central processing centre.

In the example below, all three simulated wildfires were positively identified by MAWA-1 based on analyzing temperature anomalies in the thermal image data (Image 3) and radiometric temperature data (Image 4).

Please click on the contact us button below if you like to have a discussion about MAWA-1 with the Next1 team.