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