Creation of the centre of excellence in smart forestry

Informazioni relative al progetto

Forest 4.0

ID dell’accordo di sovvenzione: 101059985

DOI

10.3030/101059985

Data della firma CE 26 Aprile 2023

Data di avvio 1 Maggio 2023

Data di completamento 30 Aprile 2029

Finanziato da

• Widening participation and spreading excellence

Costo totale

€ 9 900 000,00

Contributo UE

€ 9 900 000,00

Coordinato da

VYTAUTO DIDZIOJO UNIVERSITETAS
Lithuania

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Data Management Plan (1)

Data management plan

Produced data management plan.

Documents, reports (7)

Strategy and policies for Forest 4.0 human resources

The Director for Human Resources (HR) will be responsible for writing the HR strategy and policies and proposing them to the GB for validation. They will include:Strategy: HR management; HR planning, i.e. planning of the recruitments based on the CoE’s operational needs; Job descriptions; Salaries and benefits, rewards and compensation; Working conditions and environment.Policies: Employee rights and responsibilities; Equal employment opportunities including gender equality; HR retention; Training and talent management; Risk management, safety, hygiene, and security; Performance management and appraisal; Mobility; Relationships with unionsThe final strategy and policies approved by the GB will be published as deliverable 2.1.

Innovation support service programme

A team led by the Director for Innovation, will support research teams to engage in innovation activities.. It will be composed of individuals with backgrounds in industry and the public sector who have significant experience in research and innovation funding. The team will also put an emphasis on knowledge creation between academic and businesses, and the creation and attraction of ICT business expertise, through joint infrastructure utilisation, end-user involvement in research, skills transfer through training of industrial actors.

Overall Design of the Acceleration Programme and Planning

Overall Design of the Acceleration Programme and Planning (M10)

CoE’s 5-year strategic plan including CoE KPIs and targets

D1.5: CoE’s 5-year strategic plan (M12): details roadmap towards KPIs to be reached by the end of the project, assigns respective responsibilities to newly recruited management team.

Implementation of methodological framework for research projects

Implementation of methodological framework for research projects (M12)

Report on development of smart forestry open data infrastructure

Report on development of smart forestry open data infrastructure (M12)

Infrastructure and equipment policy

A comprehensive policy for infrastructure and equipment management will therefore be prepared by Month 12 (deliverable 3.1) and implemented afterwards by the Director for Operations.

Websites, patent fillings, videos etc. (1)

Dissemination and communication plan

An initial version of the dissemination, exploitation, and communication (DEC) plan –will be produced by M04 (deliverable 6.1). It will define the different measures to disseminate the project results, exploit them, or communicate about them. Target audiences of these measures will be described. Key performance indicators (KPIs) will be chosen, and target values defined for the first year of the project. Every year, the performance of the KPIs will be compared to the target values. Potential corrective measures will be decided for the coming year based on the performance of the previous year. These reviews and plan updates will be provided in the project progress reports by a D&C committee established on month 02. For the final review, a strategy for exploiting the project results after completion will be produced.

Pubblicazioni

Peer reviewed articles (31)

Forest 4.0 – Research Infrastructure to Support the Operationalization of Digitalization in Maintenance and Management of Forest Ecosystems

Autori: Gintautas Mozgeris; Marius Aleinikovas; Algirdas Augustaitis; Eglė Gerulaitienė; Donatas Jonikavičius; Virginija Kargytė; Gabrielė Kasputytė; Tomas Krilavičius; Nerijus Kupstaitis; Arnas Matusevičius; Silvija Misailovė-Ribokė; Martynas Narmontas; Rasa Vaitkevičiūtė
Pubblicato in: ARPHA Conference Abstracts, 2025, ISSN 2603-3925
Editore: Pensoft Publishers
DOI: 10.3897/ACA.8.E151306

Tree Segmentation from Low-Resolution Digital Orthophotos using a Hybrid Deep Learning Model

Autori: Irfan Abbas; Robertas Damaševičius; Rytis Maskeliūnas; Muhammad Abdullah Sarwar
Pubblicato in: Annals of Computer Science and Information Systems, 2025, ISSN 2300-5963
Editore: Polish Information Processing Society (PTI) / FedCSIS
DOI: 10.15439/2025F8182

Optimising the wood supply chain for enhanced furniture industry efficiency

Autori: Robertas Damaševičius, Rytis Maskeliūnas
Pubblicato in: Supply Chain Forum: An International Journal, Numero 26, 2025, ISSN 1625-8312
Editore: Informa UK Limited
DOI: 10.1080/16258312.2025.2456451

Distributed Timber Trading Mechanism Based on Blockchain Smart Contracts

Autori: Robertas Damaševičius, Rytis Maskeliūnas
Pubblicato in: Transactions on Emerging Telecommunications Technologies, Numero 36, 2025, ISSN 2161-3915
Editore: Wiley
DOI: 10.1002/ETT.70194

Real-time trajectory modelling of the mean tree volume in a forest stand through a nonsymmetric diffusion process analogy

Autori: Rupšys, P.
Pubblicato in:  Contemporary Mathematics, 2026, t. 7, nr. 1, ISSN 2705-1056
Editore: Contemporary Mathematics
DOI: 10.37256/CM.7120267962

ForestResU-Net: a hybrid deep learning model for low-resolution forest segmentation using digital orthophotos and RGB imagery

Autori: Irfan Abbas; Rytis Maskeliūnas; Ameer Hamza; Robertas Damaševičius
Pubblicato in: International Journal of Remote Sensing, 2025, ISSN 1366-5901
Editore: Taylor & Francis
DOI: 10.1080/01431161.2025.2601194

Modeling Forest Regeneration Dynamics: Estimating Regeneration, Growth, and Mortality Rates in Lithuanian Forests

Autori: Robertas Damaševičius, Rytis Maskeliūnas
Pubblicato in: Forests, Numero 16, 2025, ISSN 1999-4907
Editore: MDPI AG
DOI: 10.3390/F16020192

Journal of Sustainable Forestry

Autori: Robertas Damaševičius, Rytis Maskeliūnas
Pubblicato in: Journal of Sustainable Forestry, Numero 44, 2025, ISSN 1054-9811
Editore: Food Products Press
DOI: 10.1080/10549811.2025.2513220

Diffusion Mechanisms for Both Living and Dying Trees Across 37 Years in a Forest Stand in Lithuania’s Kazlų Rūda Region

Autori: Edmundas Petrauskas, Petras Rupšys
Pubblicato in: Symmetry, Numero 17, 2025, ISSN 2073-8994
Editore: MDPI AG
DOI: 10.3390/SYM17020213

Modelling Multi-echelon Forestry Supply Chain as a Neutrosophic Sequential Game

Autori: Robertas Damaševičius; Arianit Kurti; Rytis Maskeliūnas
Pubblicato in: Process Integration and Optimization for Sustainability, 2026, ISSN 2509-4246
Editore: Springer
DOI: 10.1007/S41660-025-00662-0

Digital transformation of the future of forestry: an exploration of key concepts in the principles behind Forest 4.0

Autori: Robertas Damaševičius, Gintautas Mozgeris, Arianit Kurti, Rytis Maskeliūnas
Pubblicato in: Frontiers in Forests and Global Change, Numero 7, 2024, ISSN 2624-893X
Editore: Frontiers Media SA
DOI: 10.3389/FFGC.2024.1424327

RNN-enriched Deep Transfer Learning Model for Audio-based Classification in Various Environmental Settings

Autori: Ahmad Qurthobi, Robertas Damaševičius, Sarmad Maqsood, Rytis Maskeliūnas
Pubblicato in: Journal of Vibration Engineering & Technologies, Numero 14, 2026, ISSN 2523-3920
Editore: Springer Science and Business Media LLC
DOI: 10.1007/S42417-025-02237-2

An analytical approach to blockchain-driven identity management in sustainable forest supply chains.

Autori: Damaševičius, Robertas; Maskeliūnas, Rytis
Pubblicato in: Supply Chain Analytics, ISSN 2949-8635
Editore: Elsevier
DOI: 10.1016/J.SCA.2025.100161

Digital transformation of the future of forestry: an exploration of key concepts in the principles behind Forest 4.0.

Autori: Damaševičius, Robertas, Mozgeris, Gintautas ir Maskeliūnas, Rytis
Pubblicato in: Frontiers in forests and global change, 2024, ISSN 2624-893X
Editore: Frontiers
DOI: 10.3389/ffgc.2024.1424327

Modeling Forest Regeneration Dynamics: Estimating Regeneration, Growth, and Mortality Rates in Lithuanian Forests

Autori: Damaševičius, Robertas; Maskeliūnas, Rytis
Pubblicato in: Forests, 2025, ISSN 1999-4907
Editore: Forests, published by MDPI (Multidisciplinary Digital Publishing Institute)
DOI: 10.3390/f16020192

Compatible basal area models for live and dying trees using diffusion processes

Autori: Petras Rupšys
Pubblicato in: Journal of Forestry Research, 2025, ISSN 1993-0607
Editore: Springer
DOI: 10.1007/S11676-025-01829-8

Machine Learning-Based Diffusion Processes for the Estimation of Stand Volume Yield and Growth Dynamics in Mixed-Age and Mixed-Species Forest Ecosystems

Autori: Petras Rupšys
Pubblicato in: Symmetry, Numero 18, 2026, ISSN 2073-8994
Editore: MDPI AG
DOI: 10.3390/SYM18010194

Adaptive Capacity of Scots Pine Trees to Meteorological Extremes in Highly Oligotrophic Soil in Hemi-Boreal Forest.

Autori: Augustaitis, A.; Sidabrienė, D.
Pubblicato in: Forests 2026, vol. 17, no 1, p. 1-25, ISSN 1999-4907
Editore: Forests
DOI: 10.3390/F17010098

Lecture Notes in Business Information Processing

Autori: Dalipi F., Öberg T., Kurti A., Ferati M
Pubblicato in: In: Rupino da Cunha P., Themistocleous M. (eds) Information Systems. EMCIS 2025. Lecture Notes in Business Information Processing, Vol. 573., ISSN 1865-1348
Editore: Springer
DOI: 10.1007/978-3-032-18487-0_11

Evolution of forest inventory and management planning system in Lithuania.

Autori: Papartė, Monika; Bikuvienė, Ina; Mozgeris, Gintautas
Pubblicato in: Baltic Forestry, 2025, vol. 31, no. 1, p. 1–20, ISSN 1392-1355
Editore: Baltic Forestry
DOI: 10.46490/BF789

Forestry Scenario Modelling: Qualitative Analysis of User Needs in Lithuania

Autori: Daiva Juknelienė, Michailas Palicinas, Jolanta Valčiukienė, Gintautas Mozgeris
Pubblicato in: Forests, Numero 15, 2025, ISSN 1999-4907
Editore: MDPI AG
DOI: 10.3390/F15030414

Adaptive sensor clustering for environmental monitoring in dynamic forest ecosystems

Autori: Robertas Damasevicius; Rytis Maskeliunas
Pubblicato in: Peer-to-Peer Networking and Applications, 2025, ISSN 1936-6450
Editore: Springer
DOI: 10.1007/S12083-025-01974-1

Sap Flow Density of the Prevailing Tree Species in a Hemiboreal Forest under Contrasting Meteorological and Growing Conditions

Autori: Algirdas Augustaitis, Ainis Pivoras
Pubblicato in: Forests, Numero 15, 2024, ISSN 1999-4907
Editore: MDPI AG
DOI: 10.3390/f15071158

Robust Forest Sound Classification Using Pareto-Mordukhovich Optimized MFCC in Environmental Monitoring

Autori: Ahmad Qurthobi, Robertas Damaševičius, Vytautas Barzdaitis, Rytis Maskeliūnas
Pubblicato in: IEEE Access, Numero 13, 2025, ISSN 2169-3536
Editore: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/ACCESS.2025.3535796

IEEE Access

Autori: Ahmad Qurthobi; Robertas Damasevicius; Vytautas Barzdaitis; Rytis Maskeliunas
Pubblicato in: IEEE Access, 2025, ISSN 2169-3536
Editore: IEEE Access
DOI: 10.1109/ACCESS.2025.3535796

Investigations of the Use of Invasive Plant Biomass as an Additive in the Production of Wood-Based Pressed Biofuels, with a Focus on Their Quality and Environmental Impact.

Autori: Gramauskas, G., Jasinskas, A., Vonžodas, T., Lemanas, E., Venslauskas, K.
Pubblicato in: Plants, 15(2), 2026, ISSN 2223-7747
Editore: Plants, 15(2)
DOI: 10.3390/PLANTS15020303

Predicting Tree Growth and Transpiration in Forests: An Analysis of a Small-Scale Dataset With Pareto Optimized Tsaug Augmentation

Autori: Rytis Maskeliūnas, Robertas Damaševičius, Modupe Odusam, Diana Sidabrienė, Algirdas Augustaitis, Gintautas Mozgeris
Pubblicato in: International Journal of Interactive Multimedia and Artificial Intelligence, Numero 9, 2026, ISSN 1989-1660
Editore: Universidad Internacional de La Rioja
DOI: 10.9781/IJIMAI.2026.6565

Forest Information Modeling: A Novel Approach to Sustainable Forest Management

Autori: Robertas Damasevicius, Rytis Maskeliunas
Pubblicato in: ICST Transactions on Scalable Information Systems, Numero 12, 2025, ISSN 2032-9407
Editore: European Alliance for Innovation n.o.
DOI: 10.4108/EETSIS.7811

Tree Detection in RGB Satellite Imagery Using YOLO-Based Deep Learning Models

Autori: Irfan Abbas, Robertas Damaševičius
Pubblicato in: Computers, Materials & Continua, Numero 85, 2025, ISSN 1546-2226
Editore: Tech Science Press
DOI: 10.32604/CMC.2025.066578

Driving Forces of Agricultural Land Abandonment: A Lithuanian Case

Autori: Daiva Juknelienė; Viktorija Narmontienė; Jolanta Valčiukienė; Gintautas Mozgeris
Pubblicato in: Land, 2025, ISSN 2073-445X
Editore: MDPI (Multidisciplinary Digital Publishing Institute)
DOI: 10.3390/LAND14040899

Enhancing Forest Security through Advanced Surveillance Applications

Autori: Danny Buchman, Tomas Krilavičius, Rytis Maskeliūnas
Pubblicato in: Forests, Numero 14, 2025, ISSN 1999-4907
Editore: MDPI AG
DOI: 10.3390/F14122335

Conference proceedings (7)

Nordic-Baltic forest growth and yield conference : August 26-28, 2025, Tartu, Estonia

Autori: Estonian University of Life Sciences
Pubblicato in: 2025
Editore: Estonian University of Life Sciences

A Hybrid Machine Learning Model for Forest Wildfire Detection using Sounds

Autori: Robertas Damaševičius, Rytis Maskeliunas, Ahmad Qurthobi
Pubblicato in: Annals of Computer Science and Information Systems, Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), Numero 39, 2024
Editore: IEEE
DOI: 10.15439/2024F7263

Forest-Inspired Reinforcement Learning Based On Nature Ecosystem Feedback Mechanisms

Autori: Rytis Maskeliūnas; Robertas Damaševičius
Pubblicato in: Annals of Computer Science and Information Systems, 2025, ISSN 2300-5963
Editore: Polish Information Processing Society (PTI) / FedCSIS
DOI: 10.15439/2025F7818

d'Alembert Convolution for Enhanced Spatio-Temporal Analysis of Forest Ecosystems

Autori: Rytis Maskeliūnas, Robertas Damaševičius
Pubblicato in: Annals of Computer Science and Information Systems, Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), Numero 39, 2024
Editore: IEEE
DOI: 10.15439/2024F4228

Kernel density estimator by minimizing bias

Autori: Ruzgas, Tomas; Pupalaigė, Kristina
Pubblicato in: 2023
Editore: KTU
DOI: 10.15388/DAMSS.14.2023

APPEND: A Blockchain-Based Model of Digital Product Passport for Furniture Industry

Autori: Fisnik Dalipi, Arianit Kurti, Mexhid Ferati
Pubblicato in: AHFE International, Human Interaction and Emerging Technologies (IHIET-AI 2024): Artificial Intelligence and Future Applications, Numero 120, 2024
Editore: AHFE International
DOI: 10.54941/AHFE1004559

Communication Papers of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Pubblicato in: Annals of Computer Science and Information Systems, 2025
Editore: Annals of Computer Science and Information Systems
DOI: 10.15439/978-83-973291-9-5