Home

TreeEyed QGIS Plugin

Version: 0.2.0

TreeEyed is a QGIS plugin to leverage AI models for tree monitoring using remote sensing imagery.

Features

This plugin seeks to integrate existing and custom AI models for tree monitoring (semantic segmentation, instance segmentation, and object detection) in high resolution RGB imagery.

Apart from the model handling this plugin facilitates the integration with QGIS layers for image extraction and post-processing. Additional features for dataset creation and validation in COCO format are available.

Available models:

• HighResCanopyHeight

• Custom Mask-RCNN

• DeepForest

• VHRTrees

Acknowledgment

This work is being carried out as part of the CGIAR Initiatives and Funded Projects. CGIAR is a global research partnership for a food-secure future. Its science is carried out by 15 Research Centers in close collaboration with hundreds of partners across the globe.

TreeEyed is an open source project, and we welcome contributions and feedback from the community. We would like to extend our gratitude to the developers and maintainers of the libraries and models integrated into this plugin:

• HighResCanopyHeight

• DeepForest

• VHRTrees

Authors

Tropical Forages Program

Alliance Bioversity International & CIAT

Research

1. 6. Ruiz-Hurtado, J. P. Bolaños, D. A. Arrechea-Castillo, and J. A. Cardoso, ‘TreeEyed: A QGIS plugin for tree monitoring in silvopastoral systems using state of the art AI models’, SoftwareX, vol. 29, p. 102071, Feb. 2025, doi: 10.1016/j.softx.2025.102071.

Citation (Bibtex format):

@article{ruiz-hurtadoTreeEyedQGISPlugin2025,
title = {{{TreeEyed}}: {{A QGIS}} Plugin for Tree Monitoring in Silvopastoral Systems Using State of the Art {{AI}} Models},
author = {{Ruiz-Hurtado}, Andres Felipe and Bola{\~n}os, Juliana Perez and {Arrechea-Castillo}, Darwin Alexis and Cardoso, Juan Andres},
year = {2025},
month = feb,
journal = {SoftwareX},
volume = {29},
pages = {102071},
issn = {2352-7110},
doi = {10.1016/j.softx.2025.102071},
keywords = {Computer vision,Deep learning,QGIS,Remote sensing,Silvopastoral systems,Tree monitoring},
}

---

References

Contents

• Home

Getting Started

• Installation
  • Requirements
  • Plugin installation
  • Required additional packages
  • Installation video
• Downloading models
  • Models
  • Download Models
  • Download Models Video
• Features
  • Inference
  • Post-process
  • Dataset
  • Validation

QuickStart

• Simple Inference
  • Simple Inference QGIS Processing Algorithms
• Tips and Tricks
  • Considerations

Examples

• Inference using HighResCanopyHeight
  • Input
  • Configuration
  • Results
• Inference using Mask-RCNN custom model
  • Input
  • Configuration
  • Results
• Inference using DeepForest model
  • Input
  • Configuration
  • Results

Developers

• Developers
  • Command Line Interface
  • Docker Usage