16 September 2024


Massive tree dieback events triggered by various disturbance agents, such as insect outbreaks, pests, fires, and windstorms, have recently compromised the health of forests in numerous countries with a significant impact on ecosystems. The inventory of forest tree dieback plays a key role in understanding the effects of forest disturbance agents and improving forest management strategies.
In this work, SWIFTT partners from the University of Bari Aldo Moro illustrate a deep learning approach that trains a U-Net model for the semantic segmentation of Sentinel-2 images of forest areas. The proposed U-Net architecture integrates an attention mechanism to amplify the crucial information and a self-distillation approach to transfer the knowledge within the U-Net architecture.
Experimental results demonstrate the significant contribution of both attention and self-distillation to gaining accuracy in two case studies in which they perform the inventory mapping of forest tree dieback caused by insect outbreaks and wildfires, respectively.
Read the paper in the link below.