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Background and Purpose

The forestry sector remains heavily reliant on fossil fuels for machinery operation, posing a significant challenge to achieving climate-neutral wood-supply. One of the main obstacles to transitioning toward renewable or low-emission fuel alternatives is the lack of a decentralized and responsive energy supply infrastructure for fuel alternatives. Effective planning for such systems requires accurate knowledge of site-specific fuel consumption, which in turn depends on a range of external and operational factors—including terrain slope, soil moisture, surface roughness, weather conditions, and seasonal variability.
This thesis project aims to harness the power of deep learning and geospatial data integration to predict energy usage and emissions in forestry operations. By developing a model that can generalize across harvesting sites with varying conditions, this work will contribute to more informed planning and benchmarking of sustainable forest management practices, particularly in regions like Northern Sweden where challenging terrain is common.

Your Task

You will develop and train a deep learning model that predicts energy consumption (and potentially emissions) based on a set of geospatial and operational parameters collected from real-world forest harvesting sites. Key aspects of your task include:

• Data preprocessing and feature engineering from existing datasets (e.g., slope, roughness, stoniness, soil water level, machine type, and weather).
• Model training and validation using deep learning frameworks (e.g., PyTorch, with potential integration of TorchGeo for handling geospatial datasets).
• Evaluation of model performance on unseen harvesting plots and analysis of generalizability.
• Contribute to an internal benchmarking tool to assess machine performance under diverse field conditions.

We recommend this project as a 30 credit Master’s thesis, given the complexity and technical depth of the task.

The degree project is to be done in English.

Your Skillset and Interests

This thesis is for example well-suited for students in -Industrial Wood Supply Management. You should have:

• A strong interest in AI, deep learning, or geospatial data analysis.
• Programming experience, preferably in Python with knowledge of deep learning libraries such as PyTorch or TensorFlow.
• Familiarity with or willingness to learn about forestry operations and terrain modeling.
• Curiosity to work at the interface of machine learning, sustainability, and field-level forestry data.
• The ability to work independently and handle real-world, possibly incomplete datasets.

What We Offer

• A relevant research topic that supports the energy transition of the Swedish forestry sector.
• Possibility of contributing to the scientific research of the department as the results of the degree project will be incorporated into a scientific publication, where the student can of course become a co-author.

For more information about the project, contact Justin Herdegen.

E-mail: justin.herdegen@slu.se

Telefon: +46722392528

Background and Purpose

The forestry sector is currently heavily dependent on fossil fuels for machinery operation. A major barrier to switching to renewable and low-emission fuel alternatives is the need for a decentralised energy supply system. In order to plan for this, it is essential to know how many forestry sites are active, where they are located, how they vary and how they develop over time.

In regions like Sweden, where clear-cut operations dominate forest harvesting practices, these worksites are highly dynamic and influenced by seasonal, economic, and environmental conditions. Yet, detailed knowledge on their spatio-temporal distribution and variation is often fragmented or unavailable.

To address this gap, this thesis project proposes a data-driven spatial analysis of forestry operations using national Vektor-map data from the Swedish Forest Agency (Skogsstyrelsen), supplemented where possible with industry data. The aim is to identify clusters and trends in worksite activity, explore site-specific differences, and establish a georeferenced database to support the future design of low-emission fuel supply chains tailored to forestry operations.

Your Task

Your main objective will be to build a comprehensive database of forestry worksites (primarily clear-cuts) and perform a spatio-temporal clustering and pattern analysis. (Recommended as a 15 Credit Bachelor-Thesis) Specifically, you will:

• Construct a structured geospatial database combining public forestry data (Skogsstyrelsen) and, where accessible, industry datasets.
• Classify and cluster forestry worksites based on characteristics such as area, harvested volume, terrain conditions, and duration.
• Identify regional patterns and assess whether specific zones consistently host more or fewer operations.
• Analyze temporal dynamics of active sites to understand seasonal, economic, or environmental influences on worksite fluctuations.

If conducted as a 30 Credit - Master-Thesis:

• Explore the link between worksites and wood storage or loading hubs, which are potential energy supply points.
• Optionally, include thinning operations in the analysis and assess how they influence spatial and temporal patterns when considered alongside clear-cuts.
• Compare patterns using similar datasets from other countries (e.g., the UK Forestry Commission’s Public Register) for a broader comparative perspective.

The degree project is to be done in English.

Your Skillset and Interests

This thesis is for example well-suited for students in Skogsvetarprogrammet // Skogsekonomiprogrammet // Industrial Wood Supply Management-program.
You should have:

•Strong interest in forest operations, logistics.
•Basic to intermediate knowledge of GIS tools (e.g., QGIS, ArcGIS).
•Experience with programming or scripting, preferably in Python.
•Familiarity with data processing and clustering techniques (e.g., SQLite, DBSCAN) is advantageous
•Motivation to work with large spatial datasets and derive practical insights from real-world operations.
•Ability to work independently and to solve problems effectively.

What We Offer

•A relevant research topic that supports the energy transition of the Swedish forestry sector.
•Possibility to contribute to the scientific research of the department as the results of the degree project will be incorporated into a scientific publication, where the student can of course become a co-author.

For more information about the project, contact Justin Herdegen.

E-mail: justin.herdegen@slu.se

Telefon: +46722392528