SWIFTT’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.

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

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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.

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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

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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

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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.

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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.

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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.

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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.

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