Computer Vision for Land Life: Geo-Mapped Forest Intelligence

Team Composition:

  • Senior Python Engineer x2
GreenTech

We developed computer vision algorithms and a data-mining infrastructure that transformed raw drone imagery into structured geospatial data. This capability enabled Land Life to accurately map tree plantings and power future reforestation analytics.

  • Machine Learning
  • AWS
  • MongoDB
  • Python
  • QGIS

Background

Land Life is an Amsterdam-based company that has been running reforestation projects in 25 countries since 2013. By using various technologies, they were able to make reforestation more affordable and efficient.

They began using drones and UAVs for mapping, which takes a fraction of the time and only 1–2 team members.

CHALLENGE

As Land Life scaled its reforestation projects across multiple regions, the team faced several limitations in how drone data could be used operationally:

  • Manual and time-consuming processing of drone imagery, which constrained scalability and insight depth.

  • Limited internal capacity to develop machine learning and computer vision solutions tailored to reforestation.

Solution

Land Life sought for a reliable partner with computer vision expertise. That’s when they approached us. Beetroot assembled a senior Python team with expertise in machine learning and computer vision to support Land Life in advancing its drone-based mapping capabilities. The focus was on building reliable ML algorithms while ensuring smooth collaboration and full transparency throughout the project.

  • ML & Computer Vision Development

    Built ML algorithms to process drone imagery and classify objects, forming the foundation for structured geospatial data extraction.

  • Infrastructure Support

    Provided local infrastructure support and conducted onboarding sessions to ensure Land Life’s team could effectively work with and extend the AI solution.

  • Transparent Delivery Process

    Used a shared online workspace to communicate progress at every stage, with regular updates on tasks, hours, and costs to maintain clarity, trust, and accountability.

Beetroot has managed the process really well and delivered what they promised. There’s a lot of transparency in working with the team. They’re realistic about what we can expect, and they’ve tailored the product to our needs. I would definitely recommend Beetroot to a colleague. They’re really flexible, and they listen to their clients. They deliver exactly what you are looking for.

Gautham Ramachandra,

Restoration Ecologist, Land Life

Results

Since partnering with Beetroot, Land Life has significantly expanded the impact of its reforestation platform.

  • The computer vision algorithms and data-mining infrastructure developed by our ML team transformed raw drone imagery into structured geospatial data, enabling precise mapping of tree plantings and forming the foundation for more advanced analytics.

  • Accurate geospatial mapping

    of all planted trees across restoration sites

  • Consistent datasets

    that support downstream geospatial analysis

  • Automated processing of drone imagery

    of drone imagery, reducing manual effort and increasing mapping throughput

  • Integration

    with digital maps and internal tools, allowing stakeholders to explore validated planting data more easily

  • This new capability moved Land Life beyond basic mapping tasks and provided the foundation for a more data-driven reforestation ecosystem.

Get expert support for your computer vision project:

Let’s discuss how Beetroot can help you build AI-powered GreenTech solutions that turn unstructured data into reliable insights and scalable analytics foundations.