jannikend/dinovtree

Model

[ECCV 2026] DINOvTree 🦖🌳

1

6 commits

1 linked in READMEs

updated Sep 4, 2026

See the code

README

[ECCV 2026] DINOvTree 🦖🌳

Project Page Paper ArXiv Benchmark Code Poster Video

This repository contains the model weights for the DINOvTree model introduced in our paper accepted at ECCV 2026:

Estimating Individual Tree Height and Species from UAV Imagery

Authors: Jannik Endres, Etienne Laliberté, David Rolnick, Arthur Ouaknine

Our model, DINOvTree, leverages a Vision Foundation Model (VFM) to extract features and predicts the height and species of the center tree in the input image with two separate heads.

Note: For full installation, training, and evaluation instructions, please refer to our GitHub repository.

📦 Model Weights & Datasets

We publish the model weights of DINOvTree in Base size for three distinct datasets.

DatasetWeights FileDescription
Quebec Treesdinovtreeb_quebectrees.pthTemperate Forest
BCIdinovtreeb_bci.pthTropical Forest
Quebec Plantationsdinovtreeb_quebecplantations.pthBoreal Plantation

⚖️ License

The VFM of our checkpoint was initialized with DINOv3 weights, which are licensed under the DINOv3 License. The heads and our changes to the VFM weights are licensed under the Apache License 2.0.

📚 Citation

If you find our work useful, please consider citing our paper:

@inproceedings{endres2026treeheightspecies,
  title     = {Estimating Individual Tree Height and Species from UAV Imagery},
  author    = {Endres, Jannik and Lalibert{\'e}, Etienne and Rolnick, David and Ouaknine, Arthur},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
biodiversity
drone-imagery
ecology
fine-grained-classification
forest-monitoring
remote-sensing
species-identification
tree-height-estimation
vision

jannikend/dinovtree

Model

[ECCV 2026] DINOvTree 🦖🌳

1

6 commits

1 linked in READMEs

updated Sep 4, 2026

See the code

README

[ECCV 2026] DINOvTree 🦖🌳

Project Page Paper ArXiv Benchmark Code Poster Video

This repository contains the model weights for the DINOvTree model introduced in our paper accepted at ECCV 2026:

Estimating Individual Tree Height and Species from UAV Imagery

Authors: Jannik Endres, Etienne Laliberté, David Rolnick, Arthur Ouaknine

Our model, DINOvTree, leverages a Vision Foundation Model (VFM) to extract features and predicts the height and species of the center tree in the input image with two separate heads.

Note: For full installation, training, and evaluation instructions, please refer to our GitHub repository.

📦 Model Weights & Datasets

We publish the model weights of DINOvTree in Base size for three distinct datasets.

DatasetWeights FileDescription
Quebec Treesdinovtreeb_quebectrees.pthTemperate Forest
BCIdinovtreeb_bci.pthTropical Forest
Quebec Plantationsdinovtreeb_quebecplantations.pthBoreal Plantation

⚖️ License

The VFM of our checkpoint was initialized with DINOv3 weights, which are licensed under the DINOv3 License. The heads and our changes to the VFM weights are licensed under the Apache License 2.0.

📚 Citation

If you find our work useful, please consider citing our paper:

@inproceedings{endres2026treeheightspecies,
  title     = {Estimating Individual Tree Height and Species from UAV Imagery},
  author    = {Endres, Jannik and Lalibert{\'e}, Etienne and Rolnick, David and Ouaknine, Arthur},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
biodiversity
drone-imagery
ecology
fine-grained-classification
forest-monitoring
remote-sensing
species-identification
tree-height-estimation
vision