Fighting fires with emerging technologies

For many years, spotting wildfires relied almost exclusively on humans on top of towers. Wildfire detection systems employing Artificial Intelligence (AI) represent a significant advancement in disaster management technology, offering the potential to save lives, protect property and preserve natural ecosystems through timely and accurate detection and response. AI-based wildfire detection systems leverage advanced technologies, including machine learning, computer vision and sensor networks, to detect and predict wildfires with higher accuracy and speed.

Data Collection, Processing and Analysis

Data is collected by satellites, drones and ground-based sensors, including cameras, temperature sensors, humidity sensors and smoke detectors placed in high-risk areas. Satellites provide large-scale monitoring through infrared and optical imagery, while drones can offer high-resolution, real-time data from specific locations.

Computer vision combines AI, machine learning and image processing techniques to identify signs of smoke, fire and other anomalies. Object detection and pattern recognition are used to distinguish between fire-related images and other disturbances such as dust thrown up during field ploughing.

Information such as wind speed, temperature and humidity is crucial, as is understanding the type and condition of vegetation. AI can use this data to assess fire risk levels and predict the spread of fires.

For example, the data from gas sensors, cameras and satellite imagery can be analyzed together to provide a comprehensive view of the wildfire. We would know where the fire started, its estimated size and its likely path and spread.

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