Forest Data 4.0: Experience and Future Development Directions

25 September 2026  |  13:00  |  Online | Webinar language: Lithuanian

We invite you to the second webinar in the Forest 4.0 online seminar series.

On 25 September at 13:00, we will present the remote sensing data collection platforms currently used and developed within Forest 4.0:

  • an ultralight aircraft equipped with integrated laser, hyperspectral and photographic scanning systems;
  • unmanned aerial vehicles equipped with LiDAR and multispectral sensors;
  • a mobile remote sensing laboratory;
  • terrestrial scanning solutions operating in SLAM mode;
  • and the upcoming development plans of Forest 4.0.

Using examples from Punia Forest, the Tauragė region and phenological monitoring, we will discuss how different platforms and sensors are selected for tasks of different scales – from detailed studies of individual objects to mapping areas of up to 20,000 hectares.

We will also share impressions from INTERGEO 2026 in Munich, highlighting the latest trends in geospatial data collection and the next development steps for Forest 4.0.

Programme

Introduction

Smart forestry starts with properly collected data

Nerijus Kupstaitis
Director of Forest 4.0

Presentation I

Large-area laser and hyperspectral scanning using an ultralight aircraft

Donatas Jonikavičius
Expert

Kęstutis Staniulis
Pilot

Presentation II

Unmanned aerial vehicles, different sensors and the mobile field laboratory

Giedrius Bosas
Expert

Presentation III

Mobile LiDAR, SLAM and future SLAM–GNSS integration

Ričardas Beniušis
Senior Researcher

Presentation IV

INTERGEO 2026 innovations and Forest 4.0 technology development plans

Monika Papartė
Researcher

Expert discussion

How to select the most suitable platform and sensor combination?

Open discussion

Questions and answers


SilvaFormer: Transformer-Based Segmentation for Forest Health Monitoring in UAV Imagery

Author(s)

Shafiullah Soomro; Rytis Maskeliūnas; Robertas Damaševičius; Arianit Kurti

Type of publication

Journal Article

Date Issued

Date Issued
2026

Publisher

IEEE

DOI

10.1109/ACCESS.2026.3712179

 


Nerijus Kupstaitis at TU Dresden: Advancing the Future of Forest 4.0

Nerijus Kupstaitis, CEO of CoE has revisited Tharandt Forest Academy since early 2000's after finishing doctoral thesis there.


In digital forestry, technology is moving fast, being part of that network it's how the field moves forward.
That's exactly why we joined the ARiF General Working Group Meeting (COST Action CA23135) at the historic Tharandt Forest Academy (TU Dresden), the cradle of forestry science since 1811. Two days of presentations, discussion and connection, including:
• HoloLens 2 for accurate DBH measurements in the forest
• Digital twin–based virtual tree tagging linking field and office
• Augmented reality projects across Europe
• Advanced forest inventory through laser scanning and 3D modelling
• Forest 4.0 digital solutions — Lithuanian examples
• A shared discussion on the future direction of the ARiF Action


Representing Lithuania and Vytautas Magnus University in this network is a real opportunity — and a group photo of all participants is coming soon. Stay tuned!


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

Author(s)

Robertas Damaševičius, Arianit Kurti, Rytis Maskeliūnas

Type of publication

Article in Process Integration and Optimization for Sustainability/span>

Date Issued

Date Issued
2026

Publisher

Springer

DOI

10.1007/s41660-025-00662-0


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

VIEW PDF

Author(s)

Rytis Maskeliūnas, Robertas Damaševičius, Modupe Odusam, Diana Sidabrienė, Algirdas Augustaitis, Gintautas Mozgeris

Type of publication

Web of Science DB article (S1)

Date Issued

Date Issued Volume Issue Start Page End Page
2026 9 6 126 141

DOI

10.9781/IJIMAI.2026.6565


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

Author(s)

Ahmad Qurthobi, Robertas Damaševičius, Sarmad Maqsood, Rytis Maskeliūnas

Type of publication

Article in Web of Science and Scopus database (S1)

Date Issued

Date Issued Volume Issue Start Page End Page
2026 14 2 1 28

Publisher

Heidelberg : Springer

Is Referenced by

Scopus

Science Citation Index Expanded (Web of Science)

URI

https://hdl.handle.net/20.500.12259/286150

DOI

10.1007/s42417-025-02237-2


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

Author(s)

Irfan Abbas, Rytis Maskeliūnas, Ameer Hamza, Robertas Damaševičius

Date Issued

Date Issued Volume Issue Start Page End Page
2025 47 3 1187 1217

Publisher

International Journal of Remote Sensing

DOI

10.1080/01431161.2025.2601194


Forest Information Modeling: A Novel Approach to Sustainable Forest Management

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Author(s)

Robertas Damaševičius and Rytis Maskeliūnas

Type of publication

Web of Science DB article (S1)

Date Issued

Date Issued Volume Issue Start Page End Page
2025 12 6 1 12

DOI

10.4108/eetsis.7811


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

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Author(s)

Irfan Abbas, Robertas Damaševičius, Rytis Maskeliūnas, Muhammad Abdullah Sarwar

Date Issued

Date Issued Volume Start Page End Page
2025 45 1 8

Publisher

FedCSIS : Annals of Computer Science and Information Systems

DOI

10.15439/2025F8182


Forest-inspired reinforcement learning based on nature ecosystem feedback mechanisms

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Author(s)

Rytis Maskeliūnas, Robertas Damaševičius

Type of publication

Article in conference proceedings in Web of Science and Scopus database (P1a)

Date Issued

Date Issued Volume Start Page End Page
2025 43 339 344

Publisher

Piscataway : Institute of Electrical and Electronics Engineers

Is Referenced by

Scopus

Conference Proceedings Citation Index – Science (Web of Science)

URI

https://hdl.handle.net/20.500.12259/283810

DOI

10.15439/2025F7818