DeepTrees🌳

Tree Crown Segmentation and Analysis in Remote Sensing Imagery with PyTorch

PyPI version DOI License: MIT CI Build

DeepTrees is a end-to-end library for tree crown semantic and instance segmentation, as well as analysis in remote sensing imagery. It provides a modular and flexible geospatial framework based on PyTorch for training, active-learning and traits analysis of tree crown segmentations. The library is designed to be easy to use and extendable, with a focus on reproducibility and scalability. It includes a variety of pre-trained models and datasets to help you get started with your analysis of tree crowns.



DeepTrees is a Dynamic Platform Project of the Integration Platform 1: “Sustainable future land use” (IP1) at the Helmholz Centre for Environmental Research (UFZ) in Leipzig, Germany.

The project aims to develop and implement deep learning models specifically for tree crown segmentation, tree trait detection and tree species classification for the federal-state (Bundesland) level Digital Orthoimages Program (DOP) at scale in Germany. The resulting tree inventories and monitoring data can be used for various applications, including forest management, biodiversity monitoring, and ecological research.

📈 Python library for active learning, segmenting, and analysing tree traits using Deep Learning models.

🌐 Maps of large scale tree segmentation datasets across germany.

📊 Training and prediction datasets of tree crowns.


Supported by:

Helmholtz AI



Cite us:

@article{khan2025deeptrees,
        author    = {Taimur Khan and Caroline Arnold and Harsh Grover},
        title     = {DeepTrees: Tree Crown Segmentation and Analysis in Remote Sensing Imagery with PyTorch},
        year      = {2025},
        journal   = {ResearchGate},
        archivePrefix = {ResearchGate},
        eprint    = {10.13140/RG.2.2.32837.36329},
        doi    = {http://dx.doi.org/10.13140/RG.2.2.32837.36329},  
        primaryClass = {cs.CV}      
      }