16 September 2025
This week SWIFTT partner Giuseppina Andresini from the University of Bari Aldo Moro presented the paper entitled “Reusing a BigEarthNet Deep Model to Map Bark Beetle Outbreaks in Sentinel-2 Forest Images” at the DEARING 2025 workshop in Porto, Portugal.
The work is authored by Vito Recchia, Giuseppina Andresini, Luca Ardito, and Annalisa Appice. Abstract: "Reusing complex deep neural models trained by leveraging a big amount of annotated data and computation resources is one of the major challenges recently addressed with the emerging Data-Centric Artificial Intelligence paradigm, to pave the way for the effective development of a Green Artificial Intelligence technology. In this paper, we consider the foundation ResNet50 model as it is pre-trained for Sentinel-2 land cover image classification in BigEarthNet. We reuse this pre-trained model as backbone of deep neural models developed for semantic segmentation, pixel image classification, and CVA in the down-stream task of mapping bark beetle outbreaks in Sentinel-2 images of forest areas. The evaluation study explores the effectiveness of the considered solutions to reuse a foundation deep model in a case study regarding forest scenes that are annotated with bark beetle outbreaks observed in September 2020 in the Czech Republic."
The 2nd International Workshop on Data-Centric Artificial Intelligence (DEARING 2025) was held on September 15, 2025 in Porto, Portugal, co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2025).
-
Next Article
Forest Threat Data Collection -
Previous Article
Webinar - Adaptive Forest Management and Policy to Tackle Climate Risks - View All Articles