Wildfire AI
THE THREAT FROM WILDFIRES IS BEING TACKLED BY AI
By Carsten Brinkschulte, CEO and Co-Founder, Dryad Networks
Greece, Italy, Spain, Turkey, the USA, Canada, Australia and even Siberia have all been wildfire hotspots in recent years. These wildfires pose a significant threat to the environment and human health. Artificial Intelligence (AI) is making significant strides in tackling the risks.
The machine learning models used for fire detection are trained on large datasets that include both fire and non-fire scenarios to accurately identify fires based on the characteristics of smoke and other factors. The models are also rigorously trained to reduce the prevalence of false positive fire alerts.
Camera detection works by using a camera on a watchtower (previously manned by people) overlooking a large area of forest.
Machine learning and AI image recognition can be used to identify the presence of smoke or fire plumes rising above the canopy. Image recognition technology has been around for some time and has proven to be effective in various applications. In the context of wildfire detection, cameras are used to capture images of the forest above the canopy and analyse them for the presence of smoke.
However, one of the main challenges with camera-based detection is the occurrence of false positives (e.g. dust when ploughing a field). Weather conditions, such as 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 is an important limitation as most human-induced fires start at the forest floor and smoke only breaches the canopy once the fire underneath has already grown quite large. The process can take up to several hours from ignition. The delay in detection can mean that by the time fire crews arrive on the scene, they are facing a dangerous job trying to contain the fire. While infrared technology could help to complement the shortcomings of optical cameras, the resolution of these camera systems is typically too low to provide usable images for detecting fires at a great distance.
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.
A major limitation of satellite-based wildfire detection lies in 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.
Billions of dollars a year are spent on fighting wildfires, protecting the trees, natural habitats, infrastructure and, of course, people. 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.
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:
Sponsors
