{"name": "TreeEyed", "package_name": "tree_eyed", "description": "TreeEyed is a QGIS plugin for tree monitoring using AI.", "about": "TreeEyed is a QGIS plugin for tree monitoring using AI.\nFeatures:\nThis plugin seeks to integrate existing and custom AI models for tree monitoring (semantic segmentation, instance segmentation, and object detection) in RGB imagery.\nDependencies:\n-opencv-python\n-onnxruntime-gpu\n-gdown\n-pycocotools\n-deepforest\n-HighResCanopyHeight\n-VHRTrees", "homepage": "https://treeeyed.readthedocs.io/en/latest/", "repository": "https://github.com/afruizh/TreeEyed", "tracker": "https://github.com/afruizh/TreeEyed/issues", "author": "Andres Felipe Ruiz-Hurtado, Tropical Forages Program - CIAT", "tags": ["vector", "python", "raster", "analysis", "digitizing", "remote sensing", "validation", "image", "machine learning", "segmentation", "canopy", "deep learning", "neural network", "satellite imagery", "automation", "dataset", "ai", "onnxruntime", "onnx", "detection", "tree monitoring", "silvopasture", "highrescanopyheight", "tree count", "maskrcnn", "canopy height", "tree crown", "silvopastoral systems", "drone imagery", "tree area", "computer vision", "deepforest", "coco", "instance segmentation", "tree", "semantic segmentation", "dinov2", "vhrtrees"], "downloads": 11739, "latest_version": "0.2.0", "versions": [{"version": "0.2.0", "experimental": false, "qgis_min": "3.0.0", "qgis_max": "3.99.0", "downloads": 4922, "uploaded_by": "afruizh", "upload_datetime": "2025-09-12T17:52:25.524691"}, {"version": "0.1.1", "experimental": false, "qgis_min": "3.0.0", "qgis_max": "3.99.0", "downloads": 4853, "uploaded_by": "afruizh", "upload_datetime": "2024-12-19T09:10:24.322635"}, {"version": "0.1", "experimental": false, "qgis_min": "3.0.0", "qgis_max": "3.99.0", "downloads": 2034, "uploaded_by": "afruizh", "upload_datetime": "2024-09-05T17:07:13.241867"}]}