We trained an AI model to identify materials present in damaged and destroyed buildings in the city of Bucha. Leveraging data on destruction from Damaged In UA, this AI-model achieved an accuracy of up to 86% in classifying materials, with some variation depending on the type.
Drone images of the damaged buildings in Bucha, along with data on the level of damage and from open sources, formed the basis for the analysis. The results are available on an interactive web platform featuring two neural network models. The latest versions of the trained model excel at identifying metal sheets, while brick identification remains the weakest at 37% due to limited visibility in the images.
The AI identified materials like slate, metal tiles, concrete, bituminous roofing, and metal sheets most frequently.
These materials from destroyed buildings hold significant potential for reuse or recycling. Concrete, metal tiles, and metal sheets offer a 100% recycling rate. However, slate requires 100% disposal due to the presence of asbestos, a hazardous carcinogenic material.
