Data Call
Data call for tree species annotations and aerial RGB or RGBI imagery (TreeAI)
Looking for data and collaborators
As part of the COST action 3DForEcoTech and COST SNSF project, we aim to establish an international collaboration and create the first database for the detection of individual tree species.
We are seeking:
- manual tree species canopy delineations.
- aerial RGBI (red, green, blue, and near infrared) or RGB imagery data for the delineated tree species from around the world.
The TreeAI database will serve as the foundation for training a universal deep-learning model for monitoring tree species using fine-grained aerial data.
We are also looking for:
- people with experience in deep learning for object detection or instance segmentation.
- people who are willing to contribute to the writing of the manuscripts.
Your benefit:
You will be given the opportunity to contribute as a co-author in
- the publication of a data manuscript (the data provided will only be published with your consent).
- a manuscript investigating different approaches to detecting individual tree species.
Consortium:
- Dr. Mirela Beloiu Schwenke, Zhongyu Xia, Prof. Verena Griess (ETH Zurich)
- Dr. Martin Mokros (University College London)
- Prof. Xinlian Liang (Wuhan University)
- Dr. Stefano Puliti (Norwegian Institute of Bioeconomy Research)
- Dr. Lars Waser, Dr. Natalia Rehush (WSL Switzerland)
- Prof. Teja Kattenborn (University of Freiburg)
- Clemens Mosig (University of Leipzig)
- Yan Cheng (University of Copenhagen)
Contact:
Zhongyu Xia, zhongyu.xia[at]usys.ethz.ch
Dr. Mirela Beloiu Schwenke, mirela.beloiu[at]usys.ethz.ch
Deadline: ongoing
The criteria for data submissions are as follows:
Join our competition!
We are organizing a data science competition for tree species identification between March and June 2025. All details are available on this page: TreeAI4Species Competition.
Dataset map
Your dataset might be the next red dot! We currently have RGB/I images and the corresponding tree species annotations from the sites represented by a red dot on this map.
