Welcome to the Resilient Forestry Project, an interdisciplinary initiative combining Computer Science and Environmental Science expertise to develop advanced UAV photogrammetry workflows for ecological management and forest monitoring. This README provides an overview of the project, the team, and the technologies used to achieve our shared vision.

Resilient Forestry aims to streamline forest inventory and ecological analysis by automating the transformation of UAV (Unmanned Aerial Vehicle) aerial imagery into actionable geospatial data. Our system leverages digital photogrammetry, 3D modeling, and interdisciplinary collaboration to optimize environmental stewardship. Key deliverables include:

• Accurate 3D canopy and terrain models.
• Automated pipelines for aerial photogrammetry and geospatial data generation.
• Performance benchmarking and accuracy assessment for UAV-acquired data.
• Integration of custom workflows for varying environmental conditions and ecosystems.

• Python: Automating workflows and implementing testing frameworks.
• WebODM: Processing UAV imagery into geospatial outputs (point clouds, orthophotos).
• QGIS: Geospatial analysis and visualization of generated models.
• GitHub: Version control and collaborative development.

• DJI Mavic 3: UAV for aerial imagery acquisition.
• Ground Control Points (GCPs): Used for georeferencing models.
• Field equipment: Measuring tree heights, diameters, and gap delineations for ground truth validation.

.
├── README.md                # Project overview and team introduction
├── src/                     # Source code for automated workflows
│   ├── preprocessing/       # Scripts for UAV image preprocessing
│   ├── modeling/            # Scripts for 3D model generation and analysis
│   └── testing/             # Unit and integration tests
├── data/                    # UAV imagery and ground truth datasets
│   ├── raw/                 # Raw UAV image data
│   ├── processed/           # Processed and georeferenced data
│   └── benchmarks/          # Benchmark results and comparison metrics
├── reports/                 # Project reports and documentation
└── docs/                    # Technical and user documentation

• Preprocess UAV-acquired imagery.
• Generate 3D models, orthophotos, and point clouds.
• Automate parameter tuning based on environmental conditions (e.g., fog, dense forest).

• Validate UAV models against ground truth measurements.
• Assess discrepancies in canopy height and gap delineation using CHM (Canopy Height Model).

• Optimize processing times for large datasets (>2000 images).
• Integrate benchmarking for pipeline performance metrics.

• Combine ecological expertise and computational tools for seamless workflow.

• Establish pipeline architecture.
• Begin testing UAV flight parameters and automating preprocessing.

• Validate 3D models using ground truth data.
• Develop and integrate benchmarking and reporting features.

• Finalize automation for end-to-end workflows.
• Deliver project results and deploy for field use.

Clone the repository:

git clone https://github.com/ResilientForestry/ProjectRepo.git
cd ProjectRepo

Install dependencies: Follow the instructions in the docs/installation.md file.

Run the pipeline:

python src/main.py --input data/raw --output data/processed

For inquiries, collaboration, or access to the repository, please submit a application. We’re excited to work together on advancing environmental stewardship with cutting-edge technology! 🌳🌍