Development of a Context-Aware AI Agent for Forest Applications Using Multi-Sensor Data

Typically, to start working on a remote sensing–based application, various analyses and insights are needed from domain experts. A significant amount of time and effort goes into preprocessing, structuring, and analyzing the data, which can be a repetitive task, especially when a multi-sensor approach is involved. This often takes away time that could otherwise be invested in innovation or research. To address this, training an LLM to understand and process the context of remote sensing tasks can improve efficiency and reduce human-induced errors.

In this work, we develop an AI agent that can reason and think like a remote sensing expert. This agent uses a RAG-based foundational model (FM) and is equipped with various image processing tools to complete a task. We use gpt-4.1-mini as the FM and the Agno framework to deploy the agent. The knowledge base provided to this agent is specially curated with relevant research articles, books, and remote sensing methodologies. This knowledge base helps the model break down a problem into logical steps that can be performed using the tools available within the agent.

These tools can download data, process it, and provide relevant statistics and visualizations. The user can prompt the agent to download multi-sensor (optical and SAR) data, perform time-series analysis for forest monitoring, and identify deforestation hotspots. The agent can fetch data from Google Earth Engine (GEE), plan processing workflows, dynamically generate Python code, and complete the prompted tasks. This approach highlights the feasibility of integrating LLMs with domain-specific knowledge bases and geospatial processing tools to create autonomous, context-aware systems. Figure 1 depicts the overall workflow of the proposed agentic system, illustrating the interaction between the user, the knowledge base, the foundational model, and the integrated processing tools. The framework is directly usable for operational forest monitoring applications and can be further fine-tuned and extended to support a broader range of environmental monitoring and geospatial analytics use cases.

                                     

                                        Figure 1: Workflow of the Agentic AI system