Axel Arzola@axelnunez68686

QNASAgent is an AI-powered platform that simplifies access to NASA Earth observation data through natural language interaction. Today, using satellite data often requires advanced technical knowledge, including scientific APIs, HDF5 and NetCDF file formats, specialized software environments, and custom data-processing scripts. This creates a major barrier for researchers, students, NGOs, journalists, and government organizations that need environmental information quickly and efficiently. QNASAgent removes this complexity by allowing users to interact with NASA satellite data as easily as asking a question. Instead of manually searching catalogs and processing raw files, users can type requests such as: “Download aerosol data over Africa and show me the map.” The platform automatically identifies the correct NASA dataset, downloads the files, processes scientific variables, stores structured information, and generates geospatial visualizations in seconds. The system integrates modern open-source technologies including LangGraph, MCP-based NASA EarthData access, local large language models, PostgreSQL, and automated HDF5 processing pipelines. This architecture enables end-to-end automation while maintaining privacy-focused local AI execution. Our target users include government agencies, universities, environmental NGOs, climate-tech startups, and data journalism organizations working on climate change, air pollution, environmental monitoring, and disaster response. By eliminating technical barriers, QNASAgent democratizes access to Earth observation data and enables non-technical users to leverage satellite intelligence for research, decision-making, education, and public communication. Our long-term vision is to create a universal natural-language interface for Earth observation systems, expanding beyond NASA data to support additional international space agencies and real-time environmental monitoring capabilities

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