Objectives of the service
The key problem for users is the difficulty in tracing the origin of wood and verifying its sustainability, often complicated by resource-intensive, slow manual audits. Furthermore, many existing solutions lack the necessary detailed, customizable analysis required to go beyond basic compliance and actively improve sustainability practices. The solution is the PicterraTrace platform, an end-to-end geospatial AI system that provides stakeholders with the core technology for a scalable and integrated solution using machine learning on Earth Observation data. The core objectives of this activity are to develop a full end-to-end solution for monitoring forestry activities, measuring carbon levels, and mitigating deforestation, and to enable the integration of this solution with existing systems via API.
Users and their needs
The service is targeted at consumer goods companies with wood or pulp&paper-derived products, forest owners cooperatives, timber suppliers, traders of deforestation-free commodities (EUDR), forest certifiers, and conservation/restoration project developers globally. Pilot users involved in the activity include certification bodies with FSC Inc, regional partners (Canada, Germany and Africa) together with their main certificate holders, conservation/restoration project developers with Nateva (former New Zealand Carbon Farming) and certifiers with Accounting For Nature, as well as consumer goods companies with Japan Tobacco International, British American Tobacco, SD Guthrie and Inditex. Their needs include: * Access to reliable and verified sustainable wood sources. * Streamlined and automated compliance with regulations like EUDR. * Improved ability to verify data, ensure compliance, and monitor large forest areas effectively. * Accurate and timely data on forest conditions and carbon stocks to support conservation and offset projects. The key challenge for the project is ensuring the solution meets verification standards (e.g., type of indicators like forest cover losses over the last ten years, forest degradation detection) and that it seamlessly integrates with user flows and platforms.
Service/ system concept
The service supplies users with critical data and insights derived from satellite imagery using a cloud-based geospatial AI solution. The capability delivered is a full view of forestry activities that can be accessed via a user-friendly web interface or integrated into existing platforms (e.g., ERP, supply chain systems) via well-documented APIs. The system works by: 1. Ingesting diverse satellite data sources (global layers and high-resolution imagery). 2. Fine-tuning custom AI models using the Picterra GeoAI Forge platform. 3. Running these models via an Analysis and Processing Engine to generate automated results on deforestation, carbon stock, forest health, and species diversity. The result is a comprehensive end-to-end solution for monitoring and risk mitigation.
Space Added Value
The solution utilizes Earth Observation (EO) imagery, specifically global/national land cover maps and high-resolution satellite imagery from 10m down to 30 cm resolution. The added value comes from combining this multi-source EO and geospatial data with the project team's advanced machine learning (ML) technology. This combination is superior to current methods because: * It provides the required scale and timeliness necessary for monitoring vast areas and tracking changes regularly, which is impractical for resource-intensive ground surveys. * It allows for the use of custom ML models on higher resolution imagery of different formats (multispectral, SAR, DEM), enabling significantly more detailed analysis and the elimination of false positives that often plague generalized, lower-resolution single open-source datasets.
Current Status
The initial phase of preparation and setup is currently underway (Months 2) after Negotiation and Kick-off Meeting. The project team is focused on securing pilot users, initial planning for workflow analysis and data processing, and finalizing requirements. The core execution phase (Solution Development, Months 3-6) will begin shortly, involving gathering data sources, refining the analysis process and workflows, and developing core components like the AI models and platform visualizations. Discussions with FSC are ongoing to refine the requirements and adapt the solution methodologies and workflows. Key tasks currently in progress include the development of user scenarios and the technical evaluation of imagery provider APIs for automated ingestion.
Prime Contractor(s)
