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