Building trust in the Restoration Movement with Python, AI, and Satellite Data

Resumen

Around the world, billions of dollars are flowing into nature-based solutions to tackle climate change, biodiversity loss, and land degradation. But ensuring that these investments deliver real, measurable results is a major challenge. At the World Resources Institute (WRI), our Restoration Monitoring team is using Python to transform how restoration progress is tracked, verified, and shared, with the goal of bringing greater trust and transparency to the movement. This talk will showcase how we combine Python’s rich ecosystem of data science, geospatial, and AI/ML libraries to power the monitoring, reporting, and verification (MRV) of restoration activities under WRI’s TerraFund program. Since 2022, TerraFund has channeled $32 million to nearly 200 restoration champions. I’ll walk through our rule-based decision support system—a Python tool built with requests and API-specific client libraries—that integrates multiple data streams to determine which projects should undergo remote or field verification. This approach helps us reduce uncertainty, optimize field missions, and improve cost planning. As part of our remote verification protocol, we deploy state-of-the-art computer vision models on high-resolution Maxar imagery to label trees and saplings with bounding boxes. Our data production process uses Python-based pipelines to query, order, and process imagery, then run inference to detect trees. This enables automated tree counting at scale, turning terabytes of imagery into actionable project-level verification. Finally, results are integrated into TerraMatch, our end-to-end platform connecting funders and restoration champions, and making insights about progress publicly accessible. Attendees will learn how Python acts as the connective tissue across APIs, AI/ML models, and geospatial workflows, and how these tools are helping us build scalable, transparent monitoring systems.

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