Making satellite data accessible with AI agents for ESG management

The untapped potential of Earth Observation and the challenges of satellite data accessibility

Earth Observation (EO) has become one of the most powerful assets in monitoring and understanding our planet. By collecting data from satellite constellations, EO technologies enable continuous observation of environmental dynamics, urban expansion, land degradation, and climate patterns. This unique capability provides decision-makers with reliable, large-scale, and time-consistent information essential for environmental management, climate adaptation, and ESG strategies.

Yet, despite its transformative potential, satellite data remains largely underutilized. One of the main barriers lies in data accessibility and usability. Raw satellite data are massive, complex, and heterogeneous. They require specific processing workflows, high computational resources, and strong technical expertise in geospatial analysis and remote sensing. For many potential users — from local administrators to sustainability managers — these requirements represent an entry barrier that limits the practical adoption of EO in operational contexts.

Another critical issue concerns data fragmentation. Information is often distributed across multiple sources, formats, and platforms, each with its own metadata standards and acquisition protocols. This makes it difficult to integrate datasets or extract actionable insights without advanced technical knowledge. Furthermore, the complexity of satellite imagery — involving radiometric calibration, atmospheric correction, and multi-spectral interpretation — adds another layer of difficulty for non-experts.

As a result, even though governments, businesses, and research institutions increasingly recognize the value of EO for sustainable decision-making, the gap between data availability and data accessibility continues to widen. Without user-friendly tools capable of translating complex geospatial data into understandable information, the potential of Earth Observation remains confined to experts and specialized institutions.

Bridging this gap requires new solutions that merge AI-driven intelligence with geospatial science, enabling automatic data interpretation, contextual analysis, and interaction in natural language. In this context, AI agents for Earth Observation emerge as a game-changing paradigm: intuitive systems capable of unlocking the true value of satellite data and making geospatial intelligence accessible to a wider audience.

How AI agents and Large Language Models make information truly accessible

Artificial Intelligence has reached a new stage of maturity with the rise of AI agents — intelligent systems capable of understanding user intent, retrieving relevant information, and performing complex tasks autonomously. These agents are powered by Large Language Models (LLMs), advanced neural networks trained on vast and diverse datasets that allow them to understand and generate human language with remarkable precision.

Unlike traditional software tools that require manual configuration and predefined workflows, AI agents act as dynamic digital assistants. They can interpret natural language questions, reason across multiple data sources, and deliver contextual answers. This makes them particularly effective in transforming highly technical domains — such as Earth Observation and geospatial data analysis — into accessible information systems.

At their core, LLMs bridge the gap between human communication and machine intelligence. They enable users to interact with data intuitively, using the same linguistic expressions they would use in a conversation. For instance, instead of navigating a complex interface or coding a geospatial query, a user can simply ask: “Which urban areas are most exposed to heat risk this summer?” The AI agent translates the question into an analytical workflow, retrieves satellite data, processes relevant layers, and returns clear, actionable results.

This paradigm shift has profound implications for ESG management and sustainable development. Companies and institutions that previously struggled to integrate satellite insights into their strategies can now leverage AI agents to automate environmental monitoring, assess climate risk, or evaluate land-use changes with minimal technical effort.

Moreover, when combined with geospatial infrastructures and cloud-based platforms, LLM-powered agents can scale efficiently — supporting real-time data processing, personalized reporting, and interactive visualizations. They don’t just simplify access to information; they transform data into knowledge, enabling informed decision-making at every level.

In the context of Earth Observation, AI agents represent the missing link between the complexity of satellite data and the simplicity of human understanding — a new way to democratize access to planetary intelligence.

AI agents for Earth Observation: turning complex geospatial data into simple insights

When applied to Earth Observation, AI agents become a powerful interface between humans and geospatial intelligence. By integrating Large Language Models with satellite data processing pipelines, these agents can interpret questions in natural language, execute multi-step analytical workflows, and deliver precise, visual, and data-driven insights.

This innovation represents a turning point for geospatial data accessibility. Traditionally, extracting information from satellite imagery required specialized software, complex algorithms, and significant expertise in GIS and remote sensing. AI agents replace this technical barrier with an intuitive, conversational approach: users simply ask a question — for example, “Where should I plant trees to reduce heat islands?” — and the system automatically orchestrates datasets, applies models, and displays the optimal areas directly on an interactive map.

Through automation and adaptive reasoning, AI agents can cross-reference multiple datasets, perform what-if simulations, and recommend evidence-based interventions. This capability is particularly relevant for ESG management, where decision-makers must rapidly assess environmental conditions, predict impacts, and plan sustainability actions.

To accelerate innovation in this field, Latitudo 40 is developing its own conversational chatbot — an AI agent designed to make satellite data analysis accessible to everyone, from city planners to corporate sustainability teams. Built on advanced orchestration frameworks and LLM technology, it translates the complexity of Earth Observation into actionable intelligence, paving the way for a new era of open, data-driven decision-making.

Latitudo 40 at ESA Acceleration Days: shaping the future of geospatial analysis

During the ESA Acceleration Days 2025 in Brussels, Latitudo 40 unveiled a groundbreaking solution that redefines the accessibility of geospatial data. Within the European Space Agency’s innovation framework, dedicated to leveraging space technologies for sustainable development, Latitudo 40 demonstrated a fully operational AI agent capable of transforming the way non-expert users access and interpret satellite data.

At the core of this innovation lies an intelligent conversational chatbot, designed to act as a digital assistant for environmental analysis. The system autonomously orchestrates all analytical steps, from retrieving and preprocessing satellite imagery to correlating datasets and visualizing results on interactive maps. This represents a paradigm shift for Earth Observation, where analytical complexity is replaced by intuitive, human-centric interaction.

The chatbot was developed using Latitudo 40’s modular architecture, based on the FastMCP framework for composable geospatial tools. This structure enables the system to dynamically select and execute the appropriate analytical modules, such as surface temperature mapping, vegetation cover estimation, flood-risk assessment, and carbon absorption analysis. Complemented by the LangGraph orchestration engine, the AI agent manages multi-step reasoning and maintains analytical state across different queries, providing coherent and context-aware responses.

To ensure flexibility and scalability, the entire system runs on a cloud-native infrastructure powered by Docker and Kubernetes. This allows seamless integration of new data sources and models, ensuring that the AI agent evolves alongside technological advancements and updated satellite missions. Additionally, Latitudo 40’s multi-model strategy guarantees adaptability across linguistic and operational contexts, enabling both English and Italian interaction for European users.

A key differentiator of this solution is its tool-first architecture: every time a new analytical capability is developed or integrated, the AI agent autonomously learns how to use it without reprogramming. This drastically reduces the time-to-market for new functionalities, positioning Latitudo 40 as a frontrunner in AI agents for Earth Observation. The result is a continuously expanding ecosystem where environmental data, algorithms, and models interoperate seamlessly to deliver actionable intelligence.

Beyond the technical achievement, the implications for ESG management are profound. Public administrations can instantly identify areas of environmental vulnerability; companies can monitor sustainability metrics in near-real time; and urban planners can simulate climate adaptation measures through conversational “what-if” analyses. By lowering the technical barriers that have long hindered satellite data adoption, Latitudo 40 is democratizing access to geospatial intelligence and empowering a broader range of stakeholders to make data-driven, sustainable decisions.

The success achieved at the ESA Acceleration Days marks a pivotal step toward a new generation of AI agents that merge human reasoning, natural language understanding, and Earth Observation intelligence. As Latitudo 40 prepares to integrate this chatbot into its upcoming Earth Data Insights platform, the company reinforces its mission to make satellite data accessible, usable, and impactful — driving a global transformation in how we perceive, analyze, and manage our planet’s resources.

Scalable, data-driven, global: the future of accessible ESG management

The convergence of AI agents, Large Language Models, and Earth Observation technologies is defining a new standard for ESG management: one where satellite data becomes truly accessible, interpretable, and actionable for everyone. By removing the dependency on specialized technical skills, these intelligent systems make it possible for organizations to harness the full value of geospatial data for sustainability reporting, climate-risk assessment, and policy design.

Through automation and contextual reasoning, AI agents for Earth Observation can integrate multi-source datasets, monitor dynamic environmental variables, and generate adaptive insights that evolve with changing conditions. This scalability is fundamental in a global context, where institutions and companies must continuously track emissions, biodiversity, and land-use patterns to align with international sustainability frameworks such as the EU Green Deal and the UN Sustainable Development Goals (SDGs).

Latitudo 40’s approach exemplifies how advanced AI-driven geospatial intelligence can accelerate this transition. By embedding its conversational agent within the forthcoming Earth Data Insights platform, the company is enabling seamless access to satellite-derived analytics at any scale — from urban-level interventions to continental climate models. This democratization of data transforms how decision-makers plan mitigation strategies, optimize resources, and measure environmental impact.

In this emerging paradigm, satellite data is no longer confined to experts; it becomes a shared resource for sustainable innovation. Scalable, data-driven, and global, this new generation of AI-powered solutions is setting the foundation for a more transparent, resilient, and intelligent management of our planet’s future.

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