The chair of Forest Resources Management (FORM) is seeking a motivated student to assist in the development of Deep Learning approaches for tree species identification. This project focuses on mapping individual trees and quantifying their species, which are critical tasks for forestry management. Using aerial RGB imagery, we aim to create a cost-effective, automated system for detecting and identifying tree species, with broad applications in forest monitoring. In collaboration with colleagues from Austria, the successful approach will be implemented in a protected area in Austria, with the goal of quantifying tree species diversity.

More details about the project can be found here: Tree Species Identification Project .

You will contribute to a project focused on automating the detection of individual tree species in forests using deep learning. Specifically, you will:

• Focus on the application of deep learning techniques in Python to process spatial and aerial data.
• Integrate various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms.
• Test deep learning models (Transformers and CNNs) for optimal accuracy using large datasets that include over 11,000 tree species annotations from across Switzerland, along with climatic, topographic, and lidar data.
• Test the best algorithm developed for the identification of tree species over a protected area in Austria using the most accurate deep learning model.

• You are a student enrolled at ETH or in a Swiss University, ideally in Geoinformatics, Machine learning, Environmental sciences, or a closely related field.
• Proficiency in programming, particularly in Python, is essential.
• Knowledge of GIS (QGIS or ArcGIS).
• Experience working with spatial data, shapefiles, raster data is required.
• Experience in processing lidar data is a plus.
• You are available for 8-12 hours per week for up to 12 months.

• a fantastic opportunity to learn while working on a concrete project.
• the opportunity to get involved in cutting edge technology and contribute to a research project.
• possibility to be involved in a publication of the outcomes.
• flexible and remote working hours.
• a working desk in the CHN building.

The position is remunerated CHF 30.70 per hour and starts in December 2024. All applications received by November 20th will receive full consideration. The position remains opened until filled.

We look forward to receiving your online application with the following documents:

• a short motivation letter
• your CV and your transcript of records

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about us can be found on our website . Questions regarding the position should be directed to,Mirela Beloiu Schwenke via email at Write an email, or to Ariane Hangartner Write an email (for administrative questions).