Urban Tree Genera Mapping in Baden-Württemberg, Germany

A research pipeline for large-scale urban tree crown detection and tree genus mapping using very-high-resolution multispectral aerial imagery and LiDAR data.

The multispectral aerial imagery and LiDAR data provided by the LGL Open GeoData-Portal https://www.lgl-bw.de/Produkte/Open-Data/

Overview

Urban Tree Genera Mapping provides an end-to-end, research-oriented workflow to:

• Download and preprocess LGL Open GeoData (multi-spectral orthophotos & nDSM).
• Build 5-channel raster tiles (RGB + NIR + normalized height).
• Perform tree crown delineation and detection
• Predict tree genera using deep learning
• Apply a teacher–student learning strategy with human-in-the-loop curation
• Scale inference to statewide coverage
• Export results as GeoPackage for GIS analysis

The code accompanies an upcoming open dataset and scientific publication on regional-scale tree genera mapping in Baden-Württemberg, Germany.

Method Workflow

Quickstart:

Clone the repository:

git clone https://github.com/GIScience/tree-genera-mapping
cd tree-genera-mapping

Create and activate a Conda environment:

conda env create -f environment.yaml
conda activate map-tree-genera

How to run the pre-trained YOLOv11l model 5CH imagery

1. Download LGL products to Generate TileDataset for selected tile ids:

python scripts/fetch_tiles.py  \
 --tile-id 32_355_6048

2. Run pre-trained YOLOv11l model to detect and classify tree genus:

python scripts/predict_yolo.py --tiles-gpkg data/tiles.gpkg --images-dir cache/tiles_5ch --model-path models/pretrained_yolov11l_tree_genus.pth --output-dir cache/initial_inference

Model Checkpoints

Task	Model Name	Modification	URL Link
Object Detection (tree + genus)	YOLO11l	5-Channel Input	yolo11l_tree_genus.pt
Object Detection (tree)	YOLO11l	5-Channel Input	yolo11l_tree.pt

Dataset & Paper

This repository accompanies:

• Dataset: {add}
• Paper: {add}

If you use this code or workflow, please cite the accompanying paper. See CITATION.cff for details.