Adaptive Chatbots for Remote Sensing Education: A Natural Language Query System to Simplify Complex Geospatial Data Understanding

NetCDF files are a standard format widely used for storing and sharing scientific data, particularly in geospatial analysis. However, analyzing these files often requires advanced programming skills, creating a steep learning curve for students and researchers without a coding background. While existing tools for NetCDF analysis are helpful, they are often optimized for specific purposes, which can limit their flexibility in addressing diverse user needs. As a result, programming languages remain the primary method to fully leverage their potential, creating a technical barrier that hampers accessibility and slows the learning process.

To address these challenges, we introduce a chatbot powered by large language models (LLMs) that offers a novel approach to remote sensing education. Designed to facilitate the analysis of NetCDF datasets, the chatbot allows users to interact directly with their own data, dynamically tailoring responses to their individual expertise levels and informational needs. It provides personalized guidance, generates Python code snippets, and offers interactive visualizations, enabling users to explore their datasets intuitively and effectively. By learning through hands-on interaction with their own data, users not only overcome technical barriers but also develop a deeper understanding of geospatial analysis techniques.

The system incorporates Retrieval-Augmented Generation (RAG) to enhance its capabilities, seamlessly integrating natural language processing with geospatial analysis tools. Unlike other generative AI solutions, such as ChatGPT, our chatbot not only prioritizes data privacy by ensuring that user datasets remain entirely local but also offers a functional advantage by generating Python code that can be executed directly when requested by the user. This feature allows users to visualize, analyze, or manipulate their data on demand, unlocking virtually unlimited possibilities for geospatial data exploration. By combining privacy, flexibility, and functionality, the chatbot becomes particularly valuable for researchers working with proprietary or confidential data, as well as for those seeking an all-in-one solution tailored to their specific needs.

Preliminary evaluations show that the chatbot significantly enhances user engagement and comprehension of complex geospatial data structures. By eliminating technical barriers and empowering users to analyze and learn directly from their own datasets, this tool represents a transformative approach to remote sensing education. It shifts the focus from navigating technical challenges to fostering discovery and deeper insights, making it an invaluable resource for both education and research.