AI is transforming satellite data analysis for environmental efforts by enabling faster, more precise monitoring, prediction, and management of natural resources and environmental conditions. Two key developments, in particular, have a significant impact on satellite data analysis operations:

1. The growing use of generative AI (GenAI) (i.e., large language models [LLMs]) for analyzing satellite data

2. In-space AI applications for real-time satellite data processing and analysis

GenAI for Satellite Data Analysis

GenAI enables faster, scalable, and more precise insights across environmental data analysis applications. Its ability to automatically detect patterns in large sets of satellite data helps reduce the need for manual data analysis, allowing end users to focus on interpreting and applying the findings as opposed to trying to figure out how to analyze vast amounts of data.

GenAI’s natural language querying capability is particularly important because it allows less-skilled or technically savvy end users to conduct complex data analysis operations. Users can interact with the data using plain language queries (e.g., “By how much has the amount of methane released into the Earth’s atmosphere increased since 2020?”) Additionally, GenAI can be used to enhance low-resolution images (for image classification), reconstruct missing data, and improve real-time monitoring, making satellite observations more readily available for critical environmental management and disaster-response scenarios. This makes the technology particularly useful for processing vast amounts of satellite imagery to identify patterns and changes in the environment that may not be easily discernible via manual (human) analysis.

Applications

GenAI can be applied to extract valuable insights from satellite data for a broad range of environmental applications, including:

• Climate change monitoring — analyzing atmospheric patterns, tracking global temperature shifts, and predicting extreme weather events

• Deforestation and land-use analysis — detecting changes in forest cover, identifying illegal logging, and monitoring agricultural expansion

• Carbon-emission tracking — identifying methane leaks and other industrial emissions to support climate mitigation strategies

• Disaster response and recovery — processing real-time satellite imagery to assess flood damage, wildfire scope, and damage from hurricanes/tornadoes, etc.

• Precision agriculture — helping farmers with irrigation planning, detecting disease in crops, and assessing soil conditions for precision/sustainable farming

• Monitoring polar ice — helping scientists and researchers track ice sheet movements/melt rates and analyzing their environmental impact

• Wildlife conservation — helping researchers, scientists, and game wardens detect changes in habitats and ecosystems and analyze their potential impact on wildlife populations

AI for Onboard Processing & Analysis of Satellite Data

AI technology is changing the way satellite data is processed and analyzed by enabling such operations to be carried out directly on board satellites in space. By integrating AI directly into satellite operations, in-space data processing reduces the latency and bandwidth constraints associated with needing to first transmit large volumes of data to Earth-based stations. This enables faster decision-making and more efficient use of resources.

Applications

Key environmental applications for applying AI to in-space data processing and analysis include:

• Environmental monitoring — allows for continuous monitoring of environmental changes, including deforestation, urbanization, and climate change, enabling more timely interventions and policy decisions

• Disaster response — can quickly analyze data from natural disasters, including wildfires, floods, and volcanic eruptions, providing real-time information to aid in emergency response efforts

• Agricultural and precision farming — can analyze satellite imagery to provide insights into crop health, moisture levels in soil, and crop-yield predictions

Companies & Organizations Using AI for Satellite Data Analysis

Several national space agencies and commercial organizations are utilizing AI to analyze satellite data to support climate monitoring, sustainability, and other environmental applications.

European Space Agency (ESA)

ESA’s Climate Change Initiative uses AI to analyze satellite data for various climate-monitoring projects, including tracking ice sheet changes, sea-level rise, and greenhouse gas concentrations. ESA’s AI-driven insights contribute to global climate models and support international climate policy.

ESA is considered a pioneer in onboard satellite AI data processing. One key initiative is Φ-lab’s AI-driven onboard processing for synthetic aperture radar (SAR) imagery. SAR satellites generate vast amounts of high-resolution radar data, which traditionally require extensive post-processing on Earth. ESA uses AI to classify SAR images directly on board satellites, reducing the volume of data that needs to be downloaded. This approach enables faster detection of environmental changes, such as deforestation, flooding, and urban expansion, making satellite-based Earth observation more efficient and responsive. (For more on ESA’s onboard AI satellite data-processing efforts, see my previous Advisor “LLMs Take Off! GenAI In Space.”)

Google

Google has developed the Earth Engine — a cloud-based geospatial platform that integrates big data, AI, and machine learning (ML) tools to analyze satellite imagery for environmental monitoring. This application hosts a multi-petabyte catalog of satellite data, enabling researchers to track deforestation, water quality, urban expansion, and other environmental factors. By combining ML with geospatial datasets, Google Earth Engine facilitates large-scale environmental research and conservation efforts, including for air-pollution detection, biodiversity assessments, and climate change analysis. Google Earth Engine is integrated with Vertex AI, Google’s AI development platform for building and using GenAI. Vertex AI can access Google’s Gemini models and various third-party GenAI models, including Anthropic’s Claude and open source LLMs (e.g., Gemma and Llama 3.2).

NASA

NASA utilizes AI to enhance satellite data analysis for a range of climate research and disaster-response applications. Its Goddard Space Flight Center’s Data Science Group has developed various GenAI models, including SatVision-TOA (Satellite Vision Top-of-Atmosphere), which processes satellite imagery to identify atmospheric features, land-cover changes, and environmental hazards. SatVision-TOA can make very accurate predictions to complete the shape of objects in obscured images and quickly identify features for analysis. It has broad applications including land-cover mapping, flood and disaster monitoring, urban planning, and environmental analysis.

In 2023, NASA collaborated with IBM to develop an AI geospatial foundation GenAI model trained on Landsat and Sentinel-2 satellite data, enabling advanced environmental monitoring. (Foundation models are a type of AI model trained on a broad set of unlabeled data and can be used for different tasks.) The goal of the NASA/IBM project was to provide an easy way for researchers to analyze and draw insights from large NASA Earth observation datasets. This model can be used for various types of environmental analyses, and NASA/IBM has made the model freely available for anyone to access.

In November 2024, NASA teamed up with Microsoft to develop Earth Copilot. This GenAI application is designed to make NASA’s vast Earth science data more accessible by leveraging Microsoft’s Azure OpenAI Service. The goal is to enable a broad range of end users — “citizen scientists,” students, educators, researchers, scientists, and policymakers — to be able to access and analyze complex, geospatial data to develop environmental insights in a friendlier and more intuitive manner. Earth Copilot simplifies the process of navigating NASA’s 100+ petabytes of Earth observation data, which is expected to grow significantly in the coming years. Earth Copilot is currently available for testing by NASA scientists and researchers, with plans to refine its capabilities before its release for wider public access.

Planet Labs

Planet Labs uses AI to enhance the pattern-recognition and anomaly-detection capabilities of its daily satellite images of the Earth’s surface. Applications include monitoring agricultural health, detecting deforestation, and assessing water resources. By offering near-real-time data analysis, the company helps organizations make informed decisions about land use and resource management.

In March 2025, Planet Labs cut a deal to use Anthropic’s Claude LLM to analyze geospatial satellite imaging data. This collaboration will combine Planet Labs’s daily geospatial data with Claude’s advanced AI capabilities, including its sophisticated reasoning and pattern-recognition abilities to analyze complex visual information at scale and uncover environmental and other insights. Planet Labs’s data represents one of the largest continuous Earth observation datasets ever created, and with Claude, could enable near-real-time pattern recognition and anomaly detection at a global scale.

Conclusion

AI is transforming satellite data analysis for environmental applications, enabling faster, more accurate insights that drive sustainable decision-making. Two major technological advancements — the rise of GenAI for satellite imagery analysis and the expansion of in-space AI for real-time data processing — are changing the way environmental data is collected and utilized. These innovations enable more efficient monitoring, prediction, and management of natural resources, thereby helping humankind better address climate challenges and other environmental issues.

Simply put, GenAI is “democratizing” access to advanced analytical tools, making satellite data analysis and interpretation more accessible to a broader range of stakeholders. AI-driven onboard processing reduces reliance on ground-based analysis, improving operational efficiency and enabling satellites to provide real-time analysis for critical environmental and natural disaster scenarios. Going forward, we should expect AI to continue to evolve, unlocking even more advanced capabilities for satellite data utilization.