Technologies for advances environmental monitoring and seismic forecasting
A classifier designed to detect cracks in images. It consists of a neural network trained on images collected from churches in the Genova area. The aim was to develop a model capable of identifying structural damage even in the presence of visual noise, such as frescoes and decorative elements. The model performs an object detection on images, producing a patch-based representation that highlights only the areas affected by cracks.
A protocol for surveying the interiors of churches in post-earthquake scenarios. The proposed methodology reduces the risks typically faced by experts who must enter potentially damaged buildings after seismic events, to assess their structural safety and usability. It leverages technologies such as robots, drones, cameras, and LiDAR systems to enable remote data acquisition, allowing operators to remain outside the building. When integrated with Product 3.2, the approach can produce a 3D model in which the damaged internal areas are clearly visualized.




Project 3