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.