Detecting Forest Wood Fire using IoT Sensor Data

50 AI engineers collaborated for 8 weeks to analyze sensor data and test possible systemic data models to develop an intelligent recognition algorithm to detect fire of different types of wood.

The project partner, Dryad Networks, is a Germany-based startup that 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 tackle wildfires, which are causing up to 20% of global CO2 emissions and have a devastating impact on biodiversity.

The problem

The world’s forests are burning! If current trends continue, up to 170 million hectares could be lost until 2030 and with it, we gradually lose the earth’s great carbon sink consuming 110 billion metric tons of CO2.

The project outcomes

The project’s goal was to build an intelligent model that will detect fire of different types of wood through analysis of the existing sensor data, thereby enabling alarms for firefighters early enough, so they can extinguish it. During the period of eight (8) weeks, the team designed and implemented several data-based pipelines, leveraging the dataset provided by the Dryad team. Combing a massive and extensive analysis of the datasets provided, together with the state-of-the-art machine learning techniques, the team delivered the following.

The results of this project lie in the state of art machine learning models and correctly classify the sensor data into two categories, “in-smoke” and “clean-air”.  The model developed in this project is scalable and replicable. Such a solution has the potential to reduce forest fires, thereby enabling alarms for firefighters early enough, so they can extinguish them. This will ultimately help to achieve the sustainable development goals in the areas of life on land and climate action.