AI-Powered Carbon Management: Insights from Planetary Data Expert

In this exclusive interview with Omdena, David Marvin, Product Lead for the Forest Ecosystems Team at Planet, shares how AI is transforming carbon management in forestry. Leveraging Planet’s daily global satellite imagery and advanced deep learning, David’s team maps forest carbon at unprecedented scale and frequency. From reducing the need for costly field measurements to enabling faster, more accurate carbon credit issuance, AI is reshaping how organizations manage carbon. This conversation dives into the practical applications, challenges, and future of AI in carbon management, offering insights for businesses looking to adopt these technologies.

Introduction: AI in Carbon Management

Managing carbon in forests has historically been a labor-intensive process, relying on field measurements that are costly, time-consuming, and limited in scope. In remote regions like the Amazon or Congo Basin, accessing forests for carbon data is often impractical. David Marvin, Product Lead for the Forest Ecosystems Team at Planet, sees AI as a game-changer for addressing these challenges. Planet operates over 250 satellites, imaging the entire Earth daily at 3-meter resolution and providing high-resolution (50 cm) task-based imagery. By combining this data with AI, David’s team creates quarterly maps of forest carbon, height, and cover, revolutionizing how carbon is monitored and managed globally.

“In carbon management, AI lets us move from sparse, expensive ground sampling to wall-to-wall measurements,” David explains. This interview explores how Planet uses AI to enhance carbon management, the benefits for clients, and the path forward for small and medium enterprises (SMEs) hesitant to adopt these technologies.

AI-Driven Carbon Mapping: From Ground to Global Scale

Traditional carbon management relies on field teams measuring tree diameters and heights, scaling these up using allometric equations to estimate carbon storage. This process is slow, expensive, and often infeasible in remote areas. David’s team at Planet uses AI to overcome these limitations, mapping forest carbon globally with deep learning models.

The journey began in 2016 at Salio Sciences, a company David co-founded, which used early deep learning to map tree heights in California by combining airborne LiDAR data with satellite imagery. “We got great results right off the bat,” David recalls, noting that this success led to a partnership with Planet and eventual acquisition in 2022. At Planet, access to daily global imagery enabled the team to scale their AI models to map forest carbon, height, and cover worldwide, updated quarterly.

These AI-driven maps provide wall-to-wall coverage, unlike plot-based sampling, which struggles with heterogeneous forests. “When you try to scale plot-based sampling, you get a lot of errors,” David says. AI models, trained on Planet’s imagery and public data like Sentinel, deliver more accurate carbon estimates over large areas, reducing the need for extensive fieldwork.

Cost and Time Savings: The Operational Impact

AI’s ability to process satellite imagery offers significant cost and time advantages for carbon management. Traditional methods require field teams to visit forests, often every five years, to collect data. In contrast, Planet’s AI system provides quarterly updates, enabling near-real-time monitoring. “You can understand changes over time in a much better way than measuring trees every five years,” David notes.

For clients, this translates to reduced costs. Sending teams to remote areas like the Amazon or Indonesia is expensive and logistically challenging. Satellite-based AI allows organizations to monitor carbon across vast areas without physical access. While field measurements remain necessary for validation, AI reduces the frequency and scale of these campaigns, saving time and resources.

Clients also benefit from faster decision-making. In carbon offset projects or commercial forestry, timely data is critical. AI-driven maps enable organizations to assess carbon stocks quickly, streamlining project planning and management. “It’s cheaper and faster than going to the field,” David emphasizes, highlighting the operational efficiency AI brings to carbon management.

Enhancing Accuracy with Local Calibration

While Planet’s global AI models achieve 85% accuracy on average, local variations can reduce precision in specific areas. “A global model might not be 85% accurate in a single stand in Washington state,” David explains. To address this, Planet is developing a feature to integrate local field or drone-based LiDAR data into their models.

By collecting minimal field samples—e.g., a dozen quick plots over a few square kilometers—clients can upload this data to Planet’s system. The AI then calibrates the global model to produce a locally tailored carbon map with higher accuracy. “You don’t need a big field campaign,” David says. “A small number of samples can significantly improve the output.”

This hybrid approach balances automation with human oversight, ensuring AI-driven maps are both scalable and precise. It also empowers clients to contribute to model improvement, creating a collaborative cycle where local data enhances global insights.

Challenges of Building AI for Carbon Management

David strongly advises against SMEs attempting to build their own AI-driven carbon maps from scratch. “It’s extremely hard to scale,” he warns. Developing these models requires expertise in forest ecology, remote sensing, and deep learning, as well as access to high-quality training data like 3D LiDAR point clouds. Processing satellite imagery into usable formats and training generalizable models is a complex, resource-intensive process.

Many organizations underestimate these challenges, assuming they can replicate Planet’s results with in-house engineers. “You can probably overfit a model for a local area, but scaling to a region like the Amazon or globally is a different story,” David says. Instead, he recommends partnering with established providers like Planet, which have spent years refining these systems. “Don’t redo the last decade of work,” he advises, urging SMEs to focus on applying existing carbon data rather than building their own.

The Future of AI in Carbon Management

The biggest barrier to widespread AI adoption in carbon management is regulatory. Standards bodies governing voluntary carbon markets have historically prohibited satellite imagery for carbon quantification due to its novelty. However, David sees this changing. “They’re recognizing the need to allow remote sensing,” he says, noting that new methodologies are emerging to permit AI-driven data in carbon credit issuance.

Over the next few years, David predicts a shift from project-based voluntary credits to jurisdictional or country-level compliance credits under frameworks like the Paris Agreement. This transition will increase demand for high-quality carbon data, which AI and satellite imagery can provide at scale. “We’ll see a huge uptick in projects at jurisdictional scales,” he forecasts, driven by relaxed restrictions on AI-based carbon maps.

Additionally, AI is streamlining the carbon credit issuance process. Traditionally, launching a carbon project takes two to five years due to rigorous documentation requirements. AI tools are reducing this to months by automating data collection and verification. “New standards will allow AI to produce the documents and datasets needed to confirm a project’s legitimacy,” David explains, envisioning a faster, more efficient market.

FAQ: AI in Carbon Management

Q: Does AI eliminate the need for field measurements in carbon management?
A: No, field measurements are still essential for validating AI models and calibrating local data. However, AI reduces the frequency and scale of field campaigns by providing wall-to-wall carbon maps.

Q: What data is required to start using AI for carbon management?
A: Organizations can leverage existing satellite imagery from providers like Planet, combined with minimal local field or drone data for calibration. The focus is on using high-quality, relevant data rather than large volumes.

Q: How quickly can SMEs see benefits from AI in carbon management?
A: With pre-built solutions like Planet’s carbon maps, SMEs can see cost and time savings within months, particularly in project planning and monitoring.

Q: What are the top use cases for AI in carbon management?
A: David recommends using AI to: 1) Map carbon stocks globally, 2) Monitor changes quarterly, 3) Streamline project documentation, 4) Prospect new project locations, and 5) Integrate disparate datasets for faster decision-making. Building custom AI models is not advised; instead, use existing carbon maps.

Q: How can SMEs measure ROI from AI adoption?
A: ROI comes from reduced field sampling costs, faster project timelines (e.g., months vs. years), and improved accuracy in carbon estimates, leading to higher-quality credits and better market competitiveness.

Q: Why should hesitant SMEs adopt AI now?
A: AI will become the industry standard within five years. Early adoption allows SMEs to learn the technology, optimize workflows, and stay ahead of competitors as standards evolve to favor AI-driven data.