tree-detection

Tree detection from aerial imagery in Python

This package implements a simple tree detector from point cloud data. It makes no assumptions about the ground plane and can handle arbitrary terrains.

A benchmark dataset for deep learning-based tree detection: VHRTrees

Instance segmentation of individual tree crowns in Mediterranean Dehesa ecosystems using YOLOv11

Code for "An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment", Rice et al. 2025

TLNet: a deep learning framework for tree detection in forest point clouds using multi-layered forest structure

Tree Detector enables you to plot trees in OpenStreetMap (OSM) extremely efficiently. Using a Faster R-CNN model, finetuned on satellite imagery of trees, Tree Detector suggests trees that a user can easily accept and plot on OSM.

Implementation and explanation of a directed graph, graph traversals, and complementary algorithms. (README in progress)

Detect and map individual tree trunks from LiDAR point clouds

Oil palm tree detection and counting from UAV/satellite imagery using YOLOv12-N, with a reproducibility-focused head-to-head comparison against YOLOv26-N.

Fine-tuning of the DeepForest model for tree detection in aerial imagery

Detection of trees from aerial image using detectron2

LiDAR tree inventory pipeline in R (lidR, sf) and PostGIS: point cloud QC, DTM, CHM, individual tree detection, and per-plot statistics with SQL.

AI-powered Smart Tree Recommendation and Identification System with Image Recognition, Tree Search, Interactive Dashboard, and Intelligent Chatbot.

Tree crown detection & canopy area estimation from aerial/satellite imagery. DeepForest + SAM2 pipeline with confidence scoring, sum-vs-union area math, and honest uncertainty reporting. Built for a Flora Carbon weekend challenge.

This repository has codes and sample data to explore new methods and analysis to implement seedling mapping using various inputs based on UAS-RGB images.

Tree-crown detection and canopy-area estimation from high-resolution forest imagery — counts trees, brackets canopy cover, and keeps its assumptions and failure cases visible

A ConvNeXt based U-net model trained to detect individual trees and clusters of trees in satellite images.

Estimates visible tree coverage from Google Street View imagery via semantic/panoptic segmentation.