SubscribeSWIFTT’s findings are communicated at scientific conferences and published in open-access journals. Find below the current list of publications.
Access all publication on the project's Zenodo community clicking here.
A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data
This paper illustrates a deep learning approach that trains a U-Net model for the semantic segmentation of Sentinel-2 images of forest areas.
G. Andresini, A. Appice and D. Malerba, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 17075-17086, 2024, doi: 10.1109/JSTARS.2024.3460981
A Multimodal Dataset for Forest Damage Detection and Machine Learning
This paper provides a useful and reliable dataset for territory of Ukraine for scientists, conservationists, foresters and other stakeholders involved in monitoring forest damage and its consequences for forest ecosystems and their services.
H. Yailymova, B. Yailymov, Y. Salii, V. Kuzin, N. Kussul and A. Shelestov, IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 2949-2953, doi: 10.1109/IGARSS53475.2024.10641873.
Conservation policies and management in the Ukrainian Emerald Network have maintained reforestation rate despite the war.
This works explores the impact of the Russian-Ukrainian War on institutional links supporting environmental sustainability and conservation efforts. Using satellite data, the authors analyzed tree cover changes in the Luhansk region’s Emerald Network protected areas from 1996 to 2020.
L. Shumilo et al., in Commun Earth Environ 4, 443 (2023). DOI: 10.1038/s43247-023-01099-4
Current Advances on Cloud-Based Distributed Computing for Forest Monitoring
This work analyzes Sentinel-2 satellite spectral channels, which distribution of pixel values was constructed for diseased and healthy forests, and the possibility of separating these two classes was analyzed based on the Bhattacharya distance.
Shelestov, A., Salii, Y., Hordiiko, N., Yailymova, H. (2023). In Lecture Notes in Networks and Systems, vol 809. Springer, Cham. DOI: 10.1007/978-3-031-46880-3_20.
DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images
This paper explores the performance of a data-centric semantic segmentation approach to detect forest tree dieback events due to bark beetle infestation in satellite images.
Andresini, G., Appice, A., Ienco, D. et al. J Intell Inf Syst (2024). doi: 10.1007/s10844-024-00877-6
Features’ Selection for Forest State Classification using Machine Learning on Satellite Data
This paper discusses the use of advanced computer vision and artificial intelligence techniques for analysing remote sensing data, specifically focusing on the semantic segmentation of forest areas.
Y. Salii, V. Kuzin, A. Lavreniuk, N. Kussul and A. Shelestov, IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 9874-9878, doi: 10.1109/IGARSS53475.2024.10642681.
Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine
This study proposes an integrated methodology and a novel framework integrating burned area mapping from Sentinel-2 data and fire risk modeling using the Fire Potential Index (FPI) in Google Earth Engine.
Yailymov B, Shelestov A, Yailymova H, Shumilo L. Fire. 2023; 6(11):411. DOI: 10.3390/fire6110411
Leveraging Sentinel-2 time series for bark beetle-induced forest dieback inventory
This work explores the performance of remote sensing methods used to perform the inventory mapping of bark beetle-induced forest dieback.
Giuseppina Andresini, Annalisa Appice, and Donato Malerba. 2024. In Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (SAC '24), 875–882. https://doi.org/10.1145/3605098.3635908
Machine Learning Models and Technology for Classification of Forest on Satellite Data
The paper deals with the problem of semantic segmentation of satellite imagery to deliver forest type map with high resolution. To solve the problem, the researchers propose 4 machine learning models.
Y. Salii, V. Kuzin, A. Hohol, N. Kussul and H. Yailymova, IEEE EUROCON 2023 - 20th International Conference on Smart Technologies, Torino, Italy, 2023, pp. 93-98, DOI: 10.1109/EUROCON56442.2023.10199006.
Monitoring of Fires Caused by War in Ukraine Based on Satellite Data
The focus of this paper is on fire monitoring studies which utilize a variety of satellite data that are used to automatically detect fires at a national level in Ukraine.
B. Yailymov, H. Yailymova, A. Shelestov and L. Shumilo, 2023 13th International Conference on Dependable Systems, Services and Technologies (DESSERT), Athens, Greece, 2023, pp. 1-5, doi: 10.1109/DESSERT61349.2023.10416520.
Potential of Spectral-Spatial Analysis to Map Forest Tree Dieback Due to Bark Beetle Hotspots in Sentinel-2 Images
This paper explores 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.
G. Andresini, A. Appice, D. Ienco, D. Malerba and V. Recchia, IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 5227-5230, doi: 10.1109/IGARSS53475.2024.10641479.
Semi-Supervised European Forest Types Mapping using High-Fidelity Satellite Data
This study introduces 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.
Bohdan Yailymov, Hanna Yailymova, Nataliia Kussul and Andrii Shelestov, in Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024.
Semi-Supervised Forest Type Mapping in Europe on Satellite Data
This work describes the results of a forest map creation within HORIZON Europe project “Satellites for Wilderness Inspection and Forest Threat Tracking” (SWIFTT) for the territory of Europe for 2022 based on Sentinel-l,2 satellite data of the Copernicus program with a high spatial resolution of 10 meters.
N. Kussul, A. Shelestov, B. Yailymov and H. Yailymova, 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Dortmund, Germany, 2023, pp. 454-458, DOI: 10.1109/IDAACS58523.2023.10348948.
SILVIA: An eXplainable Framework to Map Bark Beetle Infestation in Sentinel-2 Images
This study explores the achievements of machine learning to perform the inventory mapping of bark beetle infestation hotspots in Sentinel-2 images.
G. Andresini, A. Appice and D. Malerba, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI: 10.1109/JSTARS.2023.3312521.
Statistical methods of feature engineering for the problem of forest state classification using satellite data
In this paper, the application of Bhattacharyya distance and Spearman’s rank correlation coefficient for feature selection from satellite images was investigated.
Y.V. Salii, A.M. Lavreniuk, N.M. Kussul, System research and information technologies, 1 (2024), doi: 10.20535/SRIT.2308-8893.2024.1.07
