SubscribeFind below SWIFTT’s news articles, press releases, digests of scientific papers, as well as other outreach materials.
Publication: Semi-supervised European forest types mapping using high-fidelity satellite data
27 September 2024
In this work, SWIFTT partners from the Space Research Institute of Ukraine introduce an innovative semi-supervised approach for mapping European forest types by harnessing the power of high-resolution Sentinel-1 and Sentinel-2 satellite data from the Copernicus program.
Researchers explore novel approach to map forest dieback in satellite images
25 September 2024
Study published in the Journal of Intelligent Information Systems investigated the performance of a data-centric semantic segmentation approach to detect bark beetle infestation in satellite images. The results are part of the EU/EUSPA-funded project SWIFTT.
Publication: A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data
16 September 2024
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.
SWIFTT partners present work at ECMLPKDD 2024
13 September 2024
The flagship European machine learning and data mining conference was held in Vilnius, Lithuania, on September 9-13, 2024
Publication: A multimodal dataset for forest damage detection and machine learning
05 September 2024
In this work, SWIFTT partners from the Space Research Institute of Ukraine provide a useful and reliable dataset of Ukraine’s territory for scientists, conservationists, foresters and other stakeholders involved in monitoring forest damage and its consequences for forest ecosystems and their services.
Publication: Features’ Selection for Forest State Classification Using Machine Learning on Satellite Data
05 September 2024
In this work, SWIFTT partners from the Space Research Institute of Ukraine and collaborators discuss the use of advanced computer vision and artificial intelligence techniques for analysing remote sensing data, specifically focusing on the semantic segmentation of forest areas.
Publication: Potential of spectral-spatial analysis to map forest tree dieback due to bark beetle hotspots in Sentinel-2 images
05 September 2024
In this work, SWIFTT partners from the University of Bari Aldo Moro and collaborators explore the performance of a spectral-spatial machine learning approach used to analyse Sentinel-2 images to detect forest tree dieback events due to bark beetle infestation.
Publication: DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images
04 September 2024
In this work, SWIFTT partners from the University of Bari Aldo Moro and collaborators explore the performance of a data-centric semantic segmentation approach to detect forest tree dieback events due to bark beetle infestation in satellite images.
SWIFTT partner presents work at the 2024 IEEE International Geoscience and Remote Sensing Symposium
12 July 2024
Paper from UNIBA is titled “Potential of spectral-spatial analysis to map forest tree dieback due to bark beetle hotspots in Sentinel-2 images”.
