Data call
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 (ETH Zurich), Zhongyu Xia (ETH Zurich), Prof. Verena Griess (ETH Zurich), Prof. Xinlian Liang (Wuhan University), Dr. Stefano Puliti (Norwegian Institute of Bioeconomy Research) and Dr. Martin Mokros (University College London)
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:
- RGBI (or RGB) imagery (at ≤ 10 cm, about 3.94 in, spatial resolution) of the site.
- Ground truth tree data (n>100), including two minimum attributes:
- Geolocation of individual tree with manual delineation of the individual tree canopy (strongly preferred) AND/OR coordinates of the point of the individual tree canopy.
- Individual tree species records. The data might originate from either forests or urban regions.
Ground truth tree data: For the delineation of the tree crowns a shapefile of the individual trees with the two minimum attributes. For GPS points of the center of the tree species a shapefile or an Excel file with the X and Y coordinates and the tree species.
RGBI images: tiff file. An orthomosaic for the study region and each year is preferred, yet clips of the plots are also acceptable. Please specify the coordinate systems of the data and the acquisition date.
COCO format: A JSON annotation file with the attributes and the image files (e.g., png, jpg, or tiff).
We welcome ground truth tree data with more additional attributes, as this would facilitate further evaluations and developments of models in the future. They include, but are not limited to:
- Time of the measurements of ground truth data (multi-temporal data are welcomed).
- Tree size: diameter at breast height (cm), tree height (m), and social status i.e. dominant and co-dominant.
- Forest density: trees per unit area and basal area per unit area.
- Individual tree species defoliation and mortality (percentage 0 = no defoliation, 100 = dead).
- Environmental and/or topographic measurements: climate, soil, elevation, slope, aspect and so forth.
- Stand age, regeneration conditions (i.e., presence or absence, numbers of saplings), and management operations. For the plots with RGB/RGB-NIR images, the inclusion of high-resolution multispectral/hyperspectral images, high-resolution ground panorama photographs, UAV and/or terrestrial LiDAR point clouds data will be highly valuable.
Short explanation of the license meaning:
- Public (CC BY): Maximizes the dataset’s accessibility and usage, including commercial uses, but ensures you are credited.
- Public (CC BY) - no coordinates: The data is provided in Yolo format without coordinates, which is useful for computer vision tasks.
- Public noncommercial (CC BY-NC-ND) - Only noncommercial uses of the work are permitted, but ensures you are credited.
- Private - the data will only be used internally, not passed on or made available for download.
Note: All data is used for model training. We encourage open data; this will be our first paper's basis.
Please refer to this website for a comprehensive explanation on the license types.
Join our competition!
We will organize a data science competition for tree species identification between February and June 2025. Please express your interest in participating in the data science competition by 30 November 2024, using the form below, and we will send you more information.
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. (Last update December 2024)
