SenseNet
Technology
Our cutting-edge technology uses advanced sensors, AI algorithms, and real-time data analyses to provide early and accurate alerts.
SenseNet employs five advanced forms of technologies to tackle wildfire. Our system can significantly improve communities' safety, protecting people and land from the catastrophic effects of fire.
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Cutting-Edge Sensor
Technology
Bridging the gap between nature and technology, our sensors are finely tuned to detect early signs of wildfires.
By continuously monitoring the environment, they identify anomalies that can precede a fire outbreak.
SenseNet Approach
Environmental data is collected in real-time using a variety of highly sensitive sensors.
Advanced AI algorithms analyze sensor data to pinpoint abnormal events and ensure swift responses in critical situations.
Customized alerts are sent to our users based on different sensor readings, making sure they get prompt and relevant warnings.
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Optional Sensor Add-ons
Apart from our must-haves (temperature, humidity, air quality, volatile organic compounds, and carbon dioxide), our SensorNodes have optional add-ons sensors that can be included within the metal casing.
This includes methane, carbon monoxide, nitrogen oxides, particulate matter, and thermal sensors.
These additional sensors can aid the user in creating a more robust dataset for the specialized AI algorithm to detect irregularities rapidly and accurately.
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Fire Detection in Dense Forests
Sensors are able to detect fires in the smouldering stage even in dense forests because they do not rely on line of sight.
They are continuously collecting data to and uploading it into the cloud where the data is processed by our specialized AI. If there are any irregularities or spikes in the air composition, it can be easily detected.
For example, small fires can still be detected by thermal sensors within dense forests due to the heat signatures emitted.
Revolutionizing Detection
The Power of Our Modular AI Engine
At the heart of our technology is our modular AI engine. It uses advanced deep learning algorithms to detect environmental irregularities, while reducing the chances of false or missed alerts.
SenseNet
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Intelligent Smoke Detection Camera
Our cameras are always on alert. When our sensors pick up anomalies, the cameras zoom in for a better view.
Using AI, our system determines if the cause of the signal is regular forest activity, smoke from a campfire, or if it is early signs of a wildfire.
This close-up view gives firefighters and land management professionals a clear picture of the situation, allowing them to decide courses of best action.
Smoke
detection
from any
distance
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Satellite Image Processing
Our integration with satellite imagery is about more than just a bird’s-eye view.
By capturing real-time images from wildfire zones, we can use our AI algorithm to determine fire behaviour.
This information helps authorities develop effective management strategies.
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AI algorithm for satellite image analysis
AI obtains and analyzes data from a satellite image:
- Smoke
- Flame size
- Burned area
After analyzing the image, AI generates the following outputs:
Fire Area
3116 hectares
Fire Direction
North West
Fire Speed
5.7 mph
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Weather and Environmental Data Integration
Harnessing the power of data, we pull from different sources to build an exclusive data lake for wildfire detection and management.
This rich database offers unique insights pre-, during, and post-wildfire events.
The result? Empowering authorities with efficient and data-driven strategies.
Collect Real-Time Data
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Active fires
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Air quality information
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FWI and fire risk indices
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Rainfall and lightning information
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Direction and speed of Wind, temperature and humidity
We gather live information on ongoing fires and continuously check the wind, temperature, and humidity that can significantly affect fire behaviour.
Our system assesses fire danger levels and air quality, crucial for health protection and to provide early fire warnings to communities.
We track rainfall and lightning data to understand weather patterns and predict potential fire outbreaks caused by lightning strikes.
By harnessing this comprehensive data, we enhance situational awareness, empower decision-making, and, ultimately, strengthen our communities’ resilience against wildfires.