Actualizado el 14/09/2024
Actualizado el 14/09/2024
Getting AI involved in tackling wildfiresEscrito por Carsten Brinkschulte el 03/09/2024 a las 14:27:49846
(CEO and Co-Founder, Dryad Networks) Billions of dollars a year are spent on fighting wildfires, and by utilising AI to enhance wildfire detection, we can significantly lower wildfire risks by enabling us to detect and extinguish them in their early stages, before they have a chance to spread out of control.
We used to have people on top of towers keeping an eye on our forests, but now the work is done by cameras. However, one of the main challenges with camera-based detection is the occurrence of false positives, such as dust when ploughing a field. Haze or fog can also make it difficult for cameras to accurately identify smoke, and the time of day, particularly dawn, dusk, and nighttime, can affect the visibility of smoke in images.
By continuing to improve machine learning algorithms with more data, AI-enabled camera detection can reduce false positives and improve the accuracy of smoke detection. However, a key restriction remains that cameras typically cannot see what's happening under the tree canopy and only detect smoke plumes once they are rising above the tree canopy. This results in fire crews facing a dangerous job trying to contain an established fire.
Gas sensors, or "digital noses," can detect fires by sensing smoke beneath the canopy layer, where fires often start. These small wireless devices send alerts upon detecting smoke, allowing for quicker response times. AI models, trained on data from both fire and non-fire scenarios, help these sensors accurately identify smoke while minimising false alarms. Continuous training with diverse data, including artificial environments replicating forest fires, improves the models' accuracy and resilience. The more diverse the training data, the better the AI becomes at distinguishing between real fires and false positives.
For example, at Dryad, we constantly feed the model data about the natural, non-fire smells of a forest as well as the smell of smoke from a burning forest from our live site in Eberswalde, near Berlin. We also collect data from our many live sites across the world where the sensors are installed. All of this data is then compiled and used to constantly improve the models before pushing out an updated version to the devices, ensuring that they are always equipped with the latest detection capabilities.
Satellites also play a part in wildfire detection but they have limits regarding resolution and update frequency. Geostationary satellites provide broad coverage but with low resolution, making it challenging to detect smaller fires. Low-orbiting satellites offer higher resolution but can only update every six hours for a specific location due to Earth's rotation. While deploying hundreds of satellites could improve update frequency, it would be quite expensive considering the short lifespan of low-orbiting satellites.
Nevertheless, satellites excel in predicting wildfire development and spread by analysing factors such as terrain, wind direction, and speed. AI and machine learning can significantly enhance these predictions by processing vast amounts of data to create accurate models quickly. This information can then be relayed to firefighting and evacuation teams, aiding in the effective coordination of their efforts.
Looking forward, AI could further revolutionise wildfire response with autonomous drones. These drones, guided by AI detection systems, could quickly extinguish small fires before they escalate. This vision is already being pursued by initiatives like the XPRIZE Wildfire global challenge, which aims to develop autonomous systems capable of detecting and extinguishing fires within minutes.
In conclusion, AI and machine learning are transforming wildfire detection and prevention. By leveraging these technologies, we can improve early detection, reduce response times, and better protect our forests and communities from the devastating effects of wildfires. With ongoing advancements, the future holds even greater promise for AI in safeguarding our environment.
ABOUT THE AUTHOR Carsten Brinkschulte is CEO and co-founder of Dryad Networks. Dryad provides ultra-early detection of wildfires as well as health and growth-monitoring of forests using solar-powered gas sensors in a large-scale IoT sensor network. Dryad aims to reduce unwanted wildfires, which cause up to 20% of global CO2 emissions and have a devastating impact on biodiversity. By 2030, Dryad aims to prevent 3.9m hectares of forest from burning, preventing 1.7bn tonnes of CO2 emissions.
Website: https://www.dryad.net/ Social: https://www.facebook.com/dryadnetworks/ https://www.linkedin.com/company/dryadnetworks/
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