Managing Data Overload: AI’s Role in Planetary Monitoring & Earth Observation

(*This article is based on insights from our incredible NTC Now session featuring Maya Pindeus of Another Earth ).

Every day, our planet is monitored by hundreds of satellites, generating over 100 million gigabytes of data. This vast influx is further amplified by ground sensors, drones, and other remote sensing and monitoring technologies, creating an immense challenge: how do we extract meaningful insights from such an overwhelming volume of information?

This is where artificial intelligence steps in. AI has the potential to transform the way environmental data is analyzed at scale, unlocking new possibilities for conservation, risk management, and nature-based decision-making in general.

Maya Pindeus , CEO of Another Earth, joined the Nature Tech Collective to explore how AI is reshaping Earth observation. Together we explored what makes the application of AI in nature tech unique, the complexities of training AI models for environmental applications, the important role of synthetic data, and how different industries could leverage AI to monitor ecosystems and respond to nature-related risk.

How AI solves key business challenges in nature-based decision making

In a world where environmental conditions are evolving rapidly, organizations need to adapt to emerging threats and effectively plan for responding to scenarios that haven’t yet occurred. AI-driven Earth observation offers a powerful option for tracking these changes in real time, offering insights that can help mitigate risks, enhance resilience, and guide informed decision-making for the future:

• Agriculture & Food Security – AI can help detect and assess crop diseases, particularly for rare or high-value crops, ensuring better risk management for farmers, supply chains, and agricultural insurers.
• Disaster Response & Risk Assessment – AI can improve the ability to model natural disasters such as wildfires, floods, and landslides, helping insurers and policymakers predict future claims and refine underwriting models.
• Infrastructure Monitoring – AI-powered analysis of satellite and aerial imagery can help detect environmental threats—such as vegetation encroachment near power lines—that could lead to power outages or wildfires. Utilities and energy providers rely on these insights to minimize risks and maintain service reliability.

By providing real-time, scalable insights, AI can empower organizations to analyze past and present environmental conditions while helping to predict and prepare for future scenarios that haven’t occurred yet.

Using AI for nature data in practice: The opportunities & challenges

Nature is complex and constantly changing, which means AI models need to be tailored to a specific environment’s unique characteristics. For example, monitoring deforestation in the Amazon requires a different model than tracking desertification in arid regions.?

These factors introduce two primary challenges for applying AI to nature data:

1. The diversity of earth’s surfaces: AI models need to be trained to handle the complexity of varied ecosystems—forests, deserts, wetlands—requiring specialized datasets and models.
2. Constant flux: Nature is constantly evolving, which means AI models need to be able to adapt to new risks, trends and changes in the environment.?

For AI models to be effective, they need to be regularly trained on diverse, high-quality datasets. However, creating these datasets is costly and time-consuming, and sometimes the data we need simply doesn’t exist, especially for rare or emerging events.

What does it take to train AI models for nature data??

Training AI models to understand and process nature data is a complex and nuanced task. Unlike traditional AI applications, nature tech involves dynamic, diverse ecosystems that are constantly evolving, making it essential for AI models to be highly specialized and adaptable.

These models must not only process large volumes of environmental data, but also identify and interpret complex patterns from diverse and often incomplete datasets.

To effectively train AI models for nature-based applications, several factors need to be considered:

• Diverse & High-Quality Datasets: AI requires access to data from a wide range of sources, including satellites, sensors, drones, and ground-based observations. By integrating these various data streams, AI can create more comprehensive models that capture the full complexity of the environment. High-quality data is crucial for AI to recognize meaningful patterns and make accurate predictions.
• Balancing Specificity and Generalization: One of the key challenges in training AI for nature data is finding the right balance between specificity and generalization. While models must be able to identify highly specific environmental patterns—such as detecting an endangered species or recognizing early signs of deforestation—they also need to be flexible enough to adapt to different ecosystems and changing conditions. The ability to generalize across diverse landscapes and environments is essential for AI to remain relevant over time.
• Interpretability: For AI models to be effective in nature tech, they need to be transparent and explainable. Environmental decisions based on AI insights can have significant social, economic, and ecological impacts, which means that stakeholders—whether they are conservationists, policymakers, or local communities—must be able to trust the AI’s findings. Clear interpretability ensures that the reasoning behind AI’s conclusions is understandable, making it easier for users to apply AI-driven insights with confidence.
• Continuous Learning: Environmental conditions are constantly shifting, and AI models must keep up with these changes. Continuous learning is key to ensuring that models remain accurate and effective in responding to new risks or emerging patterns. As new data is collected, AI models need to be regularly updated to reflect the latest environmental trends and challenges. This dynamic learning process ensures that AI can adapt to the rapidly evolving landscape of nature-based challenges.

Using synthetic data to bridge gaps in nature monitoring

In nature monitoring, there are often significant gaps in the data needed to train AI models. For example, environmental data for rare or extreme events like wildfires or floods may be sparse, as these events occur infrequently in specific locations.

Similarly, certain ecosystems or regions—such as deep oceans or remote rainforests—may be difficult to monitor due to logistical challenges or inaccessibility. In these cases, real-world data may be incomplete, limited, or hard to collect, leaving AI models without the necessary information to function effectively.

One of AI's most powerful tools for nature monitoring is synthetic data. This artificially generated data is emerging as a critical part of the AI value chain in Nature Tech.

Synthetic data allows organisations to create training datasets to develop AI models that can then be used to generate insights from large amounts of Earth Observation data. Synthetic data fills in gaps where real-world observations are missing or incomplete - whether due to logistical challenges, weather conditions or rare events, and it enables precise scenario development and simulations - enabling better risk analysis and assessments.

Training AI for Rare or Extreme Events

Rare events, like wildfires or landslides, can be hard to capture in real-world data. Synthetic data can simulate these occurrences, improving AI’s predictive capabilities.

• Enhancing Satellite Imagery: Cloud cover or poor resolution can obstruct satellite images. Synthetic data can provide consistent imagery to ensure reliable environmental monitoring.
• Reducing cost and resource: Reducing the amounts and cost of real satellite imagery required for AI training
• Improving Model Robustness: Synthetic data helps train AI across diverse environmental conditions, making models more adaptable and accurate even in data-scarce areas.

While synthetic data doesn’t replace real-world observations, it complements them by creating more resilient, scalable AI models that can provide deeper insights.

Introducing Another Earth’s approach to creating synthetic data for AI model training

Another Earth uses synthetic data to improve the quality and coverage of AI models. By generating high-resolution synthetic imagery, they can better detect subtle environmental changes, such as deforestation or post-disaster anomalies. Another Earth’s approach allows AI to work with highly specialized data while maintaining flexibility across diverse use cases.

The below images visualize land cover data, synthetic images and real imagery side by side (courtesy of Another Earth):

Here’s a breakdown of how Another Earth's approach can be implemented into an AI development process .

1. Define the Goal: The first step is to clarify the objective, such as tracking species in a specific region or identifying changes after natural disasters.
2. Generate a synthetic training dataset. Leverage Another Earth's synthetic data engine to generate pixel perfect and diverse training data to train your AI model
3. Build and Train the AI Model: The dataset is used to train the model, allowing it to recognize patterns and track changes.
4. Run the Model and Extract Insights: The trained model is applied to new satellite data to generate insights, such as species counts or land-use shifts.
5. Simulate Scenarios: Leverage Another Earth’s Synthetic Data Engine to simulate different scenarios, such as different risk profiles for natural disasters or land use changes.
6. Interpret and Apply the Results: Finally, the AI-generated insights are interpreted and applied to inform decision-making and monitor conservation efforts.

Success Story: Enabling crop yield prediction in the Amazon Rainforest

Monitoring tree and plant species in a dense rainforest at scale poses a particular challenge. Individual trees and groups of plants are difficult to identify in satellite imagery, making it challenging to monitor agricultural assets. Furthermore, dense cloud coverage adds additional difficulties to monitor activity based on Earth Observation data.

Another Earth is providing high resolution synthetic datasets of tropical rainforest paired with object level detailed labels and masks. This allows their customers to build scalable crop yield prediction models for agricultural assets in tropical regions

How AI models are validated for continuous improvement

Validation ensures that AI models remain effective over time. By comparing synthetic data with real-world observations, companies like Another Earth constantly test and refine their models, ensuring they perform well across different datasets and conditions. This validation process also helps improve model interpretability, helping to ensure AI insights are understandable and trustworthy.

The future of AI for nature: This is just the beginning

While AI holds immense potential for solving environmental challenges, we are still at the beginning stages of understanding its full role in nature tech. As AI continues to evolve, its capacity to revolutionize conservation and sustainability grows, but realizing this potential requires a responsible and balanced approach.

One thing is clear: the future of AI in nature tech demands collaboration and a tailored approach to different ecosystems and regions. AI needs to be adapted to local challenges, and stakeholders—from scientists to policymakers to local communities—must be equipped with the tools and understanding to use AI insights responsibly. Education and outreach will be critical as AI becomes more integrated into nature-based solutions.

Efforts should be focused on reducing duplication of work and fostering collaboration across organizations. Open-source data as provided by Nasa’s Landsat, the Copernicus Data Space Ecosystem and the Sentinel satellites, in addition to open source platforms and consortia are likely to be key in building collective solutions, and helping to ensure data sharing and model development becomes more efficient.

The Nature Tech Collective can also play an important role here as a channel focused on nature tech stakeholder collaboration. A number of existing Nature Tech Collective members are directly supporting the drive for nature data sharing:

• Cecil provides access to a wide array of global nature datasets, managing the entire process of data acquisition and preparation. This allows organizations to focus on data analysis and the development of actionable insights without the burden of data collection logistics.

• Intertidal Agency works across sectors—governments, communities, scientists, nonprofits, and businesses—to unlock data for ocean sustainability. By bridging the gaps between ocean research, policy, and data science, they help create open, reusable frameworks for addressing future ocean conservation challenges.

Considering the environmental impact of AI

As AI’s power grows, we need to be mindful and honest about its environmental impact. Large generative AI models consume significant amounts of energy and resources.?

AI models tailored for specific environmental tasks—like those used for nature-based solutions—can potentially help to reduce this impact. In general, smaller, specialized models are more energy-efficient and produce insights that are more interpretable and transparent. Another Earth particularly advocates for this approach, emphasizing the importance of efficient, specialized AI models in driving sustainability without compromising environmental integrity.

Watch the full playback of this #NTCNow session: