Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Identification and Coverage Estimation from Ground-level Imagery

🌿Species Identification and Coverage Estimation from Ground-level Imagery for Vegetation Monitoring 📷

This repository contains the data and code used in Müller, Puliti, and Breidenbach (2025) to train and apply deep learning models for species coverage estimation using ground-level imagery.

It includes:✅ A YOLOv8 object detection model for detecting frames in images and one species instance segmentation model for species identification and identifying and segmenting species.

✅ Method to parse instance segmentation masks to species-specific coverage estimates in images.

✅ The data used to train and evaluate the models

🚀 Workflow Overview

This demo provides a step-by-step approach for training and applying the models:

1️⃣ Training:

Train two models using labeled images:

Frame Object Detection (dataset: Frame_data)

Species Instance Segmentation (dataset: Species_segmentation_data)

2️⃣ Confidence Optimization:

Optimize the confidence threshold based on downstream cover estimation performance.

3️⃣ Inference:

Predict on test images (Species_cover_data_test).

4️⃣ Evaluation:

Compare predictions with field estimates (Field_data_NFI).

📌 The code has been tested on Windows with Python 3.10.

🛠 How to Run the Demo

Follow these steps to set up and run demo.ipynb:

# Create a new environmentconda create -n VegCover python=3.10

# Activate the environmentconda activate VegCover

# Install dependenciespip install -r requirements.txt

# Install Jupyter Labpip install jupyterlab

# Open the demo notebookjupyter-lab

📖 How to Cite

If you use this work, please cite:

Müller, P., Puliti, S., & Breidenbach, J. (2025). Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Coverage Estimation from Ground-Level Imagery. Methods in Ecology and Evolution.

📜 License

This project is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later).

🔹 Key points of this license:

You are free to use, modify, and distribute the software.

If you modify and deploy this software (even as a web service), you must share your modifications under the same AGPL-3.0-or-later license.

This ensures that improvements remain open-source and benefit the community.

📖 Full license text: GNU AGPL v3.0