Creation of the centre of excellence in smart forestry

Informations projet

Forest 4.0

N° de convention de subvention: 101059985

DOI

10.3030/101059985

Date de signature de la CE 26 Avril 2023

Date de début 1 Mai 2023

Date de fin 30 Avril 2029

Financé au titre de

• Widening participation and spreading excellence

Coût total

€ 9 900 000,00

Contribution de l’UE

€ 9 900 000,00

Coordonné par

VYTAUTO DIDZIOJO UNIVERSITETAS
Lithuania

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

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.

Publications

Peer reviewed articles (20)

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

Auteurs: 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
Publié dans: ARPHA Conference Abstracts, 2025, ISSN 2603-3925
Éditeur: Pensoft Publishers
DOI: 10.3897/ACA.8.E151306

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

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

Optimising the wood supply chain for enhanced furniture industry efficiency

Auteurs: Robertas Damaševičius, Rytis Maskeliūnas
Publié dans: Supply Chain Forum: An International Journal, Numéro 26, 2025, ISSN 1625-8312
Éditeur: Informa UK Limited
DOI: 10.1080/16258312.2025.2456451

Distributed Timber Trading Mechanism Based on Blockchain Smart Contracts

Auteurs: Robertas Damaševičius, Rytis Maskeliūnas
Publié dans: Transactions on Emerging Telecommunications Technologies, Numéro 36, 2025, ISSN 2161-3915
Éditeur: Wiley
DOI: 10.1002/ETT.70194

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

Auteurs: Irfan Abbas; Rytis Maskeliūnas; Ameer Hamza; Robertas Damaševičius
Publié dans: International Journal of Remote Sensing, 2025, ISSN 1366-5901
Éditeur: Taylor & Francis
DOI: 10.1080/01431161.2025.2601194

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

Auteurs: Robertas Damaševičius, Rytis Maskeliūnas
Publié dans: Forests, Numéro 16, 2025, ISSN 1999-4907
Éditeur: MDPI AG
DOI: 10.3390/F16020192

Journal of Sustainable Forestry

Auteurs: Robertas Damaševičius, Rytis Maskeliūnas
Publié dans: Journal of Sustainable Forestry, Numéro 44, 2025, ISSN 1054-9811
Éditeur: Food Products Press
DOI: 10.1080/10549811.2025.2513220

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

Auteurs: Robertas Damaševičius; Arianit Kurti; Rytis Maskeliūnas
Publié dans: Process Integration and Optimization for Sustainability, 2026, ISSN 2509-4246
Éditeur: 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

Auteurs: Robertas Damaševičius, Gintautas Mozgeris, Arianit Kurti, Rytis Maskeliūnas
Publié dans: Frontiers in Forests and Global Change, Numéro 7, 2024, ISSN 2624-893X
Éditeur: Frontiers Media SA
DOI: 10.3389/FFGC.2024.1424327

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

Auteurs: Ahmad Qurthobi, Robertas Damaševičius, Sarmad Maqsood, Rytis Maskeliūnas
Publié dans: Journal of Vibration Engineering & Technologies, Numéro 14, 2026, ISSN 2523-3920
Éditeur: Springer Science and Business Media LLC
DOI: 10.1007/S42417-025-02237-2

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

Auteurs: Damaševičius, Robertas, Mozgeris, Gintautas ir Maskeliūnas, Rytis
Publié dans: Frontiers in forests and global change, 2024, ISSN 2624-893X
Éditeur: Frontiers
DOI: 10.3389/ffgc.2024.1424327

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

Auteurs: Petras Rupšys
Publié dans: Journal of Forestry Research, 2025, ISSN 1993-0607
Éditeur: Springer
DOI: 10.1007/S11676-025-01829-8

Forestry Scenario Modelling: Qualitative Analysis of User Needs in Lithuania

Auteurs: Daiva Juknelienė, Michailas Palicinas, Jolanta Valčiukienė, Gintautas Mozgeris
Publié dans: Forests, Numéro 15, 2025, ISSN 1999-4907
Éditeur: MDPI AG
DOI: 10.3390/F15030414

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

Auteurs: Algirdas Augustaitis, Ainis Pivoras
Publié dans: Forests, Numéro 15, 2024, ISSN 1999-4907
Éditeur: MDPI AG
DOI: 10.3390/f15071158

IEEE Access

Auteurs: Ahmad Qurthobi; Robertas Damasevicius; Vytautas Barzdaitis; Rytis Maskeliunas
Publié dans: IEEE Access, 2025, ISSN 2169-3536
Éditeur: IEEE Access
DOI: 10.1109/ACCESS.2025.3535796

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

Auteurs: Rytis Maskeliūnas, Robertas Damaševičius, Modupe Odusam, Diana Sidabrienė, Algirdas Augustaitis, Gintautas Mozgeris
Publié dans: International Journal of Interactive Multimedia and Artificial Intelligence, Numéro 9, 2026, ISSN 1989-1660
Éditeur: Universidad Internacional de La Rioja
DOI: 10.9781/IJIMAI.2026.6565

Forest Information Modeling: A Novel Approach to Sustainable Forest Management

Auteurs: Robertas Damasevicius, Rytis Maskeliunas
Publié dans: ICST Transactions on Scalable Information Systems, Numéro 12, 2025, ISSN 2032-9407
Éditeur: European Alliance for Innovation n.o.
DOI: 10.4108/EETSIS.7811

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

Auteurs: Irfan Abbas, Robertas Damaševičius
Publié dans: Computers, Materials & Continua, Numéro 85, 2025, ISSN 1546-2226
Éditeur: Tech Science Press
DOI: 10.32604/CMC.2025.066578

Driving Forces of Agricultural Land Abandonment: A Lithuanian Case

Auteurs: Daiva Juknelienė; Viktorija Narmontienė; Jolanta Valčiukienė; Gintautas Mozgeris
Publié dans: Land, 2025, ISSN 2073-445X
Éditeur: MDPI (Multidisciplinary Digital Publishing Institute)
DOI: 10.3390/LAND14040899

Enhancing Forest Security through Advanced Surveillance Applications

Auteurs: Danny Buchman, Tomas Krilavičius, Rytis Maskeliūnas
Publié dans: Forests, Numéro 14, 2025, ISSN 1999-4907
Éditeur: MDPI AG
DOI: 10.3390/F14122335

Conference proceedings (6)

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

Auteurs: Estonian University of Life Sciences
Publié dans: 2025
Éditeur: Estonian University of Life Sciences

A Hybrid Machine Learning Model for Forest Wildfire Detection using Sounds

Auteurs: Robertas Damaševičius, Rytis Maskeliunas, Ahmad Qurthobi
Publié dans: Annals of Computer Science and Information Systems, Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), Numéro 39, 2024
Éditeur: IEEE
DOI: 10.15439/2024F7263

Forest-Inspired Reinforcement Learning Based On Nature Ecosystem Feedback Mechanisms

Auteurs: Rytis Maskeliūnas; Robertas Damaševičius
Publié dans: Annals of Computer Science and Information Systems, 2025, ISSN 2300-5963
Éditeur: Polish Information Processing Society (PTI) / FedCSIS
DOI: 10.15439/2025F7818

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

Auteurs: Rytis Maskeliūnas, Robertas Damaševičius
Publié dans: Annals of Computer Science and Information Systems, Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), Numéro 39, 2024
Éditeur: IEEE
DOI: 10.15439/2024F4228

Kernel density estimator by minimizing bias

Auteurs: Ruzgas, Tomas; Pupalaigė, Kristina
Publié dans: 2023
Éditeur: KTU
DOI: 10.15388/DAMSS.14.2023

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

Publié dans: Annals of Computer Science and Information Systems, 2025
Éditeur: Annals of Computer Science and Information Systems
DOI: 10.15439/978-83-973291-9-5