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@FR-DC

FRDC

Forest Recovery Digitial Companion

🌲 FRDC: Forest Recovery Digital Companion

The Forest Recovery Digital Companion (FRDC) Project is a tool for ecologists to efficiently assess forest health, classify tree species, and monitor reforestation through the use of Unmanned Aerial Vehicles (UAVs) and Machine Learning (ML).


FRDC uses UAVs to scan large forest areas and ML to extract data on tree health, species composition, and forest recovery patterns. Traditionally, ecologists conduct field surveys to collect data on tree condition, foliage density, and presence of pests or diseases. Leveraging on FRDC, ecologists can now perform preliminary inferences on the plots of interest, to complement their traditional field-based assessments. This allows them to focus their time and expertise on more in-depth analysis and decision-making.

✨ Key Features

  • ✈️ UAV-based data collection

FRDC employs UAVs equipped with high-resolution cameras and other sensors to gather detailed information from forests. This data provides comprehensive insights into tree canopy structure, foliage condition, and other indicators of tree health and species identification.

  • 📊 Machine learning-powered analysis

FRDC utilizes ML algorithms to analyze the collected data, identifying patterns and correlations that reveal the overall health of individual trees, the forest as a whole, and the specific tree species present. We also leverage active learning, which allows the model to continually learn and adapt to improve performance.

  • 📱 Real-time collaboration platform and cross-platform app

FRDC's user-friendly cross-platform GUI serves as a central hub for ecologists to access real-time forest data and insights, and also as a tool to help track and modify metadata about trees on the go.

Apart from its utility in field surveys, FRDC's GUI also plays a crucial role in stakeholder engagement and knowledge sharing. Ecologists can generate presentations and reports using the app's data visualization features, effectively communicating their findings to stakeholders, policymakers, and the wider public. This fosters informed decision-making and promotes collaboration in forest conservation efforts.

💪 We believe that everyone should have access to our efforts, that's why we use GitHub to open-source our projects.

Pinned Loading

  1. FRDC-UI FRDC-UI Public

    Development of web-app for facilitating manual classifications of tree species

    JavaScript 1

  2. FRDC-ML FRDC-ML Public

    Forest Recover Digital Companion Machine Learning

    Python 2

  3. Tree-Classification Tree-Classification Public

    ITC Classification

    Python 1

  4. MixMatch-PyTorch-CIFAR10 MixMatch-PyTorch-CIFAR10 Public

    CIFAR10 PyTorch implementation of "MixMatch - A Holistic Approach to Semi-Supervised Learning"

    Python

  5. FRDC-Active-Learning FRDC-Active-Learning Public

    Jupyter Notebook

Repositories

Showing 7 of 7 repositories
  • FR-DC/Diversity’s past year of commit activity
    Jupyter Notebook 0 0 0 0 Updated Aug 15, 2024
  • FRDC-UI Public

    Development of web-app for facilitating manual classifications of tree species

    FR-DC/FRDC-UI’s past year of commit activity
    JavaScript 0 1 0 2 Updated Aug 8, 2024
  • FRDC-ML Public

    Forest Recover Digital Companion Machine Learning

    FR-DC/FRDC-ML’s past year of commit activity
    Python 0 MIT 2 0 0 Updated Jun 28, 2024
  • FR-DC/FRDC-Active-Learning’s past year of commit activity
    Jupyter Notebook 0 0 0 0 Updated May 17, 2024
  • .github Public
    FR-DC/.github’s past year of commit activity
    0 MIT 0 0 0 Updated May 13, 2024
  • MixMatch-PyTorch-CIFAR10 Public

    CIFAR10 PyTorch implementation of "MixMatch - A Holistic Approach to Semi-Supervised Learning"

    FR-DC/MixMatch-PyTorch-CIFAR10’s past year of commit activity
    Python 0 MIT 0 0 0 Updated Nov 27, 2023
  • Tree-Classification Public

    ITC Classification

    FR-DC/Tree-Classification’s past year of commit activity
    Python 0 1 0 0 Updated Sep 17, 2023

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