Forest Data 4.0: Experience and Future Development Directions
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
Smart forestry starts with properly collected data
Nerijus Kupstaitis
Director of Forest 4.0



Large-area laser and hyperspectral scanning using an ultralight aircraft
Donatas Jonikavičius
Expert
Kęstutis Staniulis
Pilot
Unmanned aerial vehicles, different sensors and the mobile field laboratory
Giedrius Bosas
Expert


Mobile LiDAR, SLAM and future SLAM–GNSS integration
Ričardas Beniušis
Senior Researcher
INTERGEO 2026 innovations and Forest 4.0 technology development plans
Monika Papartė
Researcher

How to select the most suitable platform and sensor combination?
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
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
Predicting Tree Growth and Transpiration in Forests: An Analysis of a Small-Scale Dataset With Pareto Optimized Tsaug Augmentation
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
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
Science Citation Index Expanded (Web of Science)
URI
| https://hdl.handle.net/20.500.12259/286150 |
DOI
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
Forest Information Modeling: A Novel Approach to Sustainable Forest Management
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
Tree Segmentation from Low-Resolution Digital Orthophotos using a Hybrid Deep Learning Model
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
Forest-inspired reinforcement learning based on nature ecosystem feedback mechanisms
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
Conference Proceedings Citation Index – Science (Web of Science)
URI
| https://hdl.handle.net/20.500.12259/283810 |
DOI

