What challenges did Fairatmos face before AI implementation?

Mapping carbon projects manually requires remote-sensing specialists several weeks per project, limiting scalability and slowing revenue opportunity assessment. Ensuring compliance with government regulations and international standards for carbon mapping to build client trust.

What was the impact of the AI solution?

Reduced project assessment times from weeks to seconds, enabling rapid demonstration of carbon credit potential and revenue predictions. Improved model accuracy and transparency, increasing client confidence and adoption of carbon projects. Expanded project coverage significantly from few thousand hectares to entire Southeast Asia region. Supported business growth plans and enhanced operational efficiency with cloud AI and data services.

Source

GCP

Fairatmos AI and Earth Engine for Carbon Project Mapping in Sustainability

Use case typeSustainability analyticsUpdated Jun 13, 2026

Fairatmos, an Indonesian sustainability startup, revolutionizes carbon project mapping by integrating Google Earth Engine, BigQuery, and Vertex AI to accelerate multi-dimensional spatial data analysis and model experimentation for nature-based carbon emission reduction projects across Southeast Asia.

Organization: Fairatmos
Industry: Other
Location: Indonesia

Reported outcomes

Strategic outcomes

Speed & agilityAccelerated carbon project assessmentCustomer experience & trustImproved client confidence and adoptionMarket & geographic expansionExpanded project coverage across Southeast AsiaNew product / capabilityEnabled richer spatial data analysis

Primary read

Use case focus

Showing 2 of 2

1AI for Environmental Sustainability
2Geospatial Data Analysis

Mapping carbon projects manually requires remote-sensing specialists several weeks per project, limiting scalability and slowing revenue opportunity assessment.
Ensuring compliance with government regulations and international standards for carbon mapping to build client trust.

Implemented Google Earth Engine to securely manage spatial datasets spanning 450 million hectares.
Integrated Earth Engine with BigQuery to run complex queries combining spatial, government, and public data for richer insights.
Leveraged Vertex AI to experiment with and improve land-cover classification machine learning models.
Adopted a centralized solution replacing manual, laptop-based map digitization, improving accuracy and speed.

Technologies

Google Earth Engine BigQuery Vertex AI

Reduced project assessment times from weeks to seconds, enabling rapid demonstration of carbon credit potential and revenue predictions.
Improved model accuracy and transparency, increasing client confidence and adoption of carbon projects.
Expanded project coverage significantly from few thousand hectares to entire Southeast Asia region.
Supported business growth plans and enhanced operational efficiency with cloud AI and data services.

Implementation partners1

CloudMile

Sources & evidence1

Groundedness: 4/5

Primary source

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Customer evidence