A QGIS plugin for tree monitoring using AI.
This plugins 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.
| Model | Source | Preferred spatial resolution |
|---|---|---|
| HighResCanopyHeight | https://github.com/facebookresearch/HighResCanopyHeight | 1 m |
| Mask R-CNN | Custom trained | 4.77 m |
| Deepforest | https://github.com/weecology/DeepForest | less than 0.5 m |
TreeEyed plugin is now available directly in the QGIS Python Plugins Repository and can be installed using the plugin manager in QGIS.
Documentantion and tutorials are available here.
This plugin works on QGIS, and it was tested on Windows using QGIS 3.28.9-Firenze.
It requires additional python packages that can be installed by using the plugin and following the installation instructions:
- rasterio
- pycocotools
- torch
- torchvision
- opencv-python
- deepforest
A dependencies folder with the required packages will be added in the plugin root folder.
This repository is licensed under the Apache 2.0 license.
Andrés Felipe Ruiz-Hurtado, Tropical Forages - CIAT
