iFOS News
The Future of Timber Harvesting: iFOS is now Part of the CO2For-IT Project
We are excited to announce our participation in the research project “CO2For-IT”. This initiative is dedicated to the development, prototypical implementation, and practical testing of a “Forest Data Space”. Our goal is to revolutionize sustainable and climate-positive timber production. At the heart of “CO2For-IT” lies the “Forest Data Space”. This platform is built on the principles of openness, federation, trustworthiness, and security, as advocated by the European Gaia-X initiative. It will serve as a foundation for comprehensive sustainability monitoring, covering both the biological production in forests and the technical production processes.
The “Forest Data Space” is a game-changer, offering new, extensive incentives for digital and sovereign participation to forestry actors who have traditionally operated in analog or semi-digital modes. This initiative promises to catalyze digital transformation across the entire sector, enabling for the first time the comprehensive optimization of value creation processes and networks, from saplings to wood-based products.
This platform will enable cross-value chain monitoring of CO2 balances. It will provide crucial data for developing strategies to combat climate change, positioning the “Forest Data Space” as an enabling technology for the green digital transformation of forestry value chains through data-driven services.
A significant part of our contribution to the “CO2For-IT” project involves precise carbon accounting during timber harvesting and transportation, based on individual log sections. For this purpose, we are deploying both an advanced version of our smart chainsaw and our iFOS software in harvesters. In this endeavor, the seamless integration of machinery enabled by our iFOS middleware system across the entire timber harvesting process is key to ensuring accurate carbon tracking.
Furthermore, we are focusing on the physical and emotional stress faced by humans during timber harvesting. Finally, we will enable the deployment of a more advanced forest road condition monitoring sensor system “Messlanze”, which will be specifically designed for timber trucks. Optimizing truck routes means less fuel consumption and a smaller carbon footprint, aligning perfectly with our commitment to sustainability and environmental responsibility.
Project Partners:
We are proud to collaborate with a consortium of esteemed partners in this project:
- Materna Information & Communications SE
- RIF Institut für Forschung und Transfer e. V.
- RWTH Aachen University, including the Institute for Man-Machine Interaction (MMI), the Laboratory for Machine Tools and Production Engineering (WZL), and the Institute for Industrial Engineering (IAW)
- Rhenus Forest Logistics GmbH & Co. KG
- HSM Hohenloher Spezial Maschinenbau GmbH & Co. KG
- Forstliches Forschungs- und Kompetenzzentrum Gotha (ThüringenForst – AöR)
- Kuratorium für Waldarbeit und Forsttechnik e. V. (KWF)
- foldAI
Together with these partners, we look forward to advancing sustainable forestry practices and contributing to the fight against climate change through the “CO2For-IT” project.
BSc. / MSc. Thesis [position filled]
The Potential of Smart Chainsaws in Motor-manual Timber Harvesting
Location: remote / Roding (Bavaria)
THIS POSITION HAS BEEN FILLED. DO NOT APPLY!
As part of the “Smart Forestry” project funded by the German Agency for Renewable Resources (FNR), we are developing and integrating digital twins into large forestry machines as well as hand-held equipment such as chainsaws.
Your topic: Determination of the Potential of Smart Chainsaws in Motor-manual Timber Harvesting
In a time study, STIHL’s flagship chainsaw MS500i equipped with advanced sensor technology is tracked during logging. Information about the execution of the work (work phases), but also about the work objects (attributes of the trees to be felled, attributes of the produced stem sections) will be recorded. In the course of your scientific work, you will analyze these data sets in different respects, evaluate them statistically or geostatistically, and visualize them. You can build on previous research activities and on the expertise of iFOS GmbH. You will receive professional support from an in-house mentor at any time.
Requirements
- BSc. or MSc. student in the field of forest science / forest engineering or comparable
- knowledge in the field of motor-manual harvesting
- interest in time studies
- knowledge in the field of statistics
- knowledge in the field of GIS
If you are…
- reliable,
- can work independently and in a structured manner,
- like to venture into new and unknown territory without hesitation,
…we offer:
- working in a young, dynamic company,
- personal freedom and flexible working hours,
- space for creativity in solving technical issues,
- the possibility of subsequent employment in interesting projects with impact in the forestry and environmental sector.
iFOS and Smart Forestry
iFOS GmbH is an internationally operating industry-related research, engineering, and advisory company in the field of forestry. We cooperate closely with renowned partners in German and European forestry such as equipment manufacturers, universities, state forests, and wood manufacturers.
You will contribute to the Smart Forestry project funded by the German Agency for Renewable Resources (FNR). The goal of this research project is to develop cross-cluster approaches for intelligent, fully-integrated forest harvesting based on Forestry 4.0 principles. We cross-link all actors along the wood harvesting network with digital twins. These freely configurable networks enable all actors to communicate more effectively and on equal terms. The aim is to improve control, valuation, and optimization of the forest harvesting process for everyone involved and enables integration with related processes.
Are you excited to transform forestry?
- Then prepare your CV and point out how you match the requirements listed above,
- e-mail your application documents to lukas.schreiber[[at]]ifos-gmbh.de
- and we will get back to you within a week to schedule an interview.
We are looking forward to hear from you!
THIS POSITION HAS BEEN FILLED. DO NOT APPLY!
Master’s Thesis or Internship [position filled]
Smart Chainsaw and Machine Learning
Location: remote / Roding (Bavaria)
THIS POSITION HAS BEEN FILLED. DO NOT APPLY!
You are excited about applying cutting-edge research in a practical context?
You want to help make the forest industry fit for the digital age?
Then this is the opportunity for you!
Your Topic: Machine Learning in a Chainsaw’s Digital Twin
Your internship or thesis revolves around STIHL’s flagship chainsaw MS500i, equipped with additional sensors to measure engine parameters and its motion in 3D-space. In addition, this chainsaw has Bluetooth connections to interface with other actors in the harvesting chain in near-real-time.
You will develop machine learning models to infer actionable information from the sensor data. This will involve the following steps:
- prepare chainsaw sensor data from a real wood harvesting operation for analysis;
- train machine learning models to infer the workers’ actions and the locations of cut timber sections from the sensor data;
- validate your models using UAV and other data;
- provide guidance on how to deploy these machine learning models to resource-limited devices in the forest.
You can rely on our expertise and experience in forestry, geographic information systems, machine learning, and so on. A mentor will be ready to support you anytime.
If you …
- are enrolled in a scientific or technical master’s degree program;
- are fluent in at least one programming language and keen to work with Python;
- have taken a course in machine learning;
- know the basics of coordinate transformations in three dimensions;
- have applied numerical analysis techniques to solve problems during your studies;
- are a self-starter and excited to learn;
… we offer you:
- work in a young, dynamic company;
- personal freedom and self-determined working hours;
- space for creativity in solving technical issues;
- opportunities to get to know project partners such as STIHL, RWTH Aachen’s Institute for Man-Machine Interaction, the Bavarian State Forests, and others;
- the chance to continue with a permanent job with impact in the forestry and environmental sector.
iFOS and Smart Forestry
iFOS GmbH is an internationally operating industry-related research, engineering and advisory company in the field of forestry. We cooperate closely with renowned partners in German and European forestry such as equipment manufacturers, universities, state forests and wood manufacturers.
You will contribute to the Smart Forestry project funded by the German Agency for Renewable Resources (FNR). The goal of this research project is to develop cross-cluster approaches for intelligent, fully-integrated forest harvesting based on Forestry 4.0 principles. We cross-link all actors along the wood harvesting network with digital twins. These freely configurable networks enable all actors to communicate more effectively and on equal terms. The aim is to improve control, valuation and optimization of the forest harvesting process for everyone involved and enables integration with related processes.
Are you excited to transform forestry?
- Then prepare your CV and point out how you match the requirements listed above,
- e-mail your application documents to lukas.schreiber[[at]]ifos-gmbh.de
- and we will get back to you within a week to schedule an interview.
We are looking forward to hear from you!
THIS POSITION HAS BEEN FILLED. DO NOT APPLY!
Insights from Drone Data for the Chainsaw Field Study
The utilization of processed photogrammetric drone data significantly contributes to unlocking valuable insights in our field study. We collected a comprehensive and detailed dataset of drone images. Proper planning and execution enabled us to cover a vast area in little time. We processed these images through the EDEO photogrammetry processing pipeline to generate highly accurate three-dimensional models of the study area.
By leveraging the georeferenced 3D models, we gain a deeper understanding of the spatial distribution of the forest and many required tree attributes such as canopy cover and vegetation density, but also tree heights and stem section measurements. These insights provide essential context for interpreting the data stream of the smart STIHL chainsaw in different areas within the study site.
Unveiling a Treasure Chest of Data
After the initial days of operation, the preliminary examination of the raw data reveals a promising and unique treasure chest which the iFOS team will evaluate in the upcoming weeks.
The sensors integrated in the chainsaw delivered valuable data, which was streamed directly to laptops for further analysis. This data collection process will provide comprehensive insights that enable us to examine critical parameters such as spatial positioning of the stem sections, wood dimensions and working efficiency.
The cooperation between our research team, the STIHL development team, the forest management of BaySF, and the dedicated forest workers played a vital role in the success of this very first field test. Their expertise and support greatly contributed to the smooth execution of the study and the acquisition of high-quality data.
The analysis process will involve precise examination of the recorded data, extracting patterns, trends, and correlations to derive meaningful insights and potential optimizations. We will use this information to develop software for deducing harvesting data from the raw data in real-time. We are eager to share our findings and contribute to the continuous evolution of smart forestry practices. Stay tuned for forthcoming updates as we embark on the evaluation phase.
Drone-Assisted Reference Data Collection for Smart Chainsaw Field Study
In the pursuit of enhancing the Smart Chainsaw field study, the integration of drone flights will further improve our data collection. With careful planning, we conducted drone flights over two study plots, amassing over 12 000 overlapping photos in the last two days. These images will be utilized to create accurate georeferenced photogrammetric models of the field plots. The drone-acquired reference data will be crucial in assessing the spatial positioning of the felled stem sections, as well as their dimensions.
With this valuable dataset at our disposal, we are excited to analyze and synthesize the information, further advancing the development and optimization of smart forest practices.
Field Study: Smart Chainsaw at BaySF’s Forstbetrieb Roding
The ongoing field study featuring the smart chainsaw is progressing well. Our current focus is testing the chainsaw in collaboration with “Forstbetrieb Roding” of BaySF, specifically in mixed forest stands with strong tree diameters. The recorded work steps include cutting a felling notch, making a felling cut, delimbing, and performing separation cuts. Raw data captured by the saw’s integrated sensor box is streamed to laptops and recorded for analysis.
The cooperation between our team and the forest management and workers of BaySF has been excellent. In the event of any issues with the prototypes, the STIHL developers are readily available by phone to provide quick assistance.
After the initial days of operation, a preliminary examination of the raw data reveals a promising and unique treasure chest which the iFOS team will evaluate in the upcoming weeks.
Smart STIHL Chainsaw – Upcoming Field Study
Our first field study in the Smart Forestry project is about to begin in the coming days. In this globally unique experiment, we will test a smart chainsaw from STIHL equipped with advanced capabilities. This innovative MS500i prototype chainsaw can detect its position and motion in space and simultaneously record important parameters from the powertrain, including engine speed.
The main objective of our study is to derive production parameters for the harvested wood. Similar to a harvester head, the chainsaw will accurately record the location and assortment of every stem section. This information will be invaluable within the context of Industry 4.0, as it can be shared instantly with other participants in the harvesting chain, such as skidders, forwarders and so on. This enables the seamless transfer of data regarding the dimension and position of the log sections.
We are excited to collaborate closely with esteemed partners including STIHL, BaySF, and RWTH Aachen University in the upcoming days for this field study.
