SubscribeFind below SWIFTT’s news articles, press releases, digests of scientific papers, as well as other outreach materials.
SWIFTT Newsletter #4
03 March 2025
The fourth issue of the SWIFTT project newsletter is out! Get up to date with the project development and partner activities.
SWIFTT consortium meeting in Paris, France
27 February 2025
Partners discussed the accomplishments in the project's second year and the plans for the future.
Timbtrack provides training to Forestry France
24 February 2025
Field training in data collection with the SWIFTT mobile app happened in Col de la Moreno, France.
Timbtrack provides field training with the SWIFTT mobile app to EcoTree
07 February 2025
The company provided invaluable feedback on the SWIFTT mobile app.
Publication: An attention-based CNN approach to detect forest tree dieback caused by insect outbreak in Sentinel-2 images
28 January 2025
In this work, SWIFTT partners from the University of Bari Aldo Moro propose a deep learning-based approach, named AVALON, that implements a CNN to detect tree dieback events in Sentinel-2 images of forest areas.
Timbtrack provides field training in data collection with the SWIFTT mobile app to Rīgas Meži’s foresters
15 January 2025
This data collection process is not only important for testing the platform and the associated apps, and to train machine-learning models
SWIFTT project highlighted in video by the EU Agency for the Space Programme
08 January 2025
EUSPA supports the market development of the EU Space downstream sector and the uptake of space-based solutions through its Horizon Europe calls.
SWIFTT highlighted at the Da Vinci Dialogues
27 November 2024
Event co-organised by project partner Da Vinci Labs and La Fabrique du Futur was held on November 26th, in Paris, France.
SWIFTT partner presents work at the 27th International Conference on Discovery Science
17 October 2024
Research presented proposes a deep learning-based approach, named AVALON, that implements a CNN to detect tree dieback events in Sentinel-2 images of forest areas.
