Global Initiative
Building the first global database for tree species annotation using high-resolution aerial images and deep learning.
Catalogue
A 30 m national map resolves continuous basal-area composition of main tree species in Switzerland.
Individual tree crowns annotated by species across Swiss sites, paired with high-resolution aerial imagery.
Species-level annotations across the Eisenwurzen forests in Austria, built for training and benchmarking vision models.
Nationwide mapping of standing dead trees, a key indicator of forest health and biodiversity.
Monthly Sentinel-2 indicator showing where Swiss vegetation is doing better or worse than its long-term average.
Tree species distribution crossed with monthly vegetation condition: where each species grows, and how it is doing there.
Deep-learning detections of dead trees in Sihlwald across a decade of aerial imagery, filterable by confidence and size.
A global database for tree species and tree mortality annotation paired with high-resolution aerial and drone imagery from sites worldwide.
Robust and generalizable deep learning models for individual tree species identification in sub-meter resolution aerial imagery.
Advancing forest mapping and biodiversity monitoring through AI-based solutions that scale across continents and ecosystems.
Data Call
Each point is a species already in the database. The nearer the centre, the more datasets record it. The open rim is where new species land.
Academic & Research Partners
Attribution
If you use the TreeAI dataset or any contributing sub-dataset in your research, please cite the following.
Full record, version history, and file downloads on Zenodo.