Hotspot detection
Identify wildfire ignition points in real time using satellite data.
AI-powered wildfire prediction and monitoring
Context
Wildfires are increasing in frequency and intensity, putting ecosystems, infrastructure, and lives at risk. Traditional monitoring methods often fail to provide timely insights needed to prevent escalation.
We usually work best with teams who know building software is more than just shipping code.
Government agencies managing forest and disaster response
Forestry departments monitoring large land areas
Environmental organisations tracking wildfire risks
Infrastructure operators near high-risk zones
Small teams without need for real-time monitoring
Organisations relying only on manual reporting
Projects without access to satellite or sensor data
One-time wildfire analysis use cases
Problem framing
Many systems rely on delayed satellite data or manual reporting, making early detection difficult. Continuous data from satellites and sensors is hard to process at scale. Without predictive models, teams lack visibility into high-risk zones and potential fire spread, leading to slower response and higher damage.
Manual reporting of fire incidents
Delayed satellite image analysis
Standalone monitoring tools with limited integration
Reactive response after fire outbreaks
Late detection of wildfire outbreaks
No continuous analysis of incoming data
Limited visibility into high-risk areas
Lack of predictive insights for fire spread
Delivery scope
Structured building blocks we use to de-risk delivery and keep enterprise programs predictable.
Identify wildfire ignition points in real time using satellite data.
Forecast fire movement using machine learning and environmental factors.
Visualise high-risk zones with heatmaps and layered data views.
Incorporate climate data to improve spread accuracy and predictions.
Combine ground sensor data like temperature and smoke for early signals.
Provide real-time alerts and unified dashboards for monitoring and response.
Ingest satellite, weather, and sensor data continuously
Process geospatial data for real-time analysis
Apply AI models for risk prediction and spread simulation
Deliver insights through dashboards and automated alerts
We build intelligent platforms that combine satellite imagery, IoT sensors, and AI models. Our systems continuously analyse environmental data to detect risks early, predict fire spread, and provide clear, actionable insights through unified dashboards.
Measurable results teams plan for when we ship the full stack, integrations, and governance together.
Earlier detection of wildfire risks
Faster and more coordinated response efforts
Reduced environmental and economic damage
Improved planning with predictive insights
Share scope, constraints, and timelines. We respond with a clear delivery approach, not a generic pitch deck.
Start the conversationStraight answers procurement and engineering teams ask before a build kicks off.
MODIS, VIIRS, Sentinel-2, Landsat, and custom data providers.
Yes. Satellite + IoT enables monitoring even in isolated regions.
Yes. Alerts are triggered for hotspots, anomalies, and high-risk zones.
Yes. Our AI integrates wind, humidity, terrain, and vegetation.
Yes. Dashboards are responsive and accessible on mobile devices.
Short answers if you are deciding who builds and supports this kind of work.
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Share your details with us, and our team will get in touch within 24 hours to discuss your project and guide you through the next steps