SatQuery Al: Building a Conversational System for Satellite-Image Analysis
SatQuery AI: Building a Conversational System for Satellite-Image Analysis I built SatQuery AI around a simple idea: I wanted users to interact with satellite and Earth-observation imagery through natural language rather than having to translate every question into a sequence of specialized image-processing and geospatial operations. A user should be able to ask: “Where has vegetation decreased?”…
SatQuery AI aims to enable users to interact with satellite imagery using natural language instead of specialized image processing techniques. A user can ask questions like "Where has vegetation decreased?" or "What changed between these two satellite images?" The system separates understanding the user's query from executing the satellite analysis.
The natural language processing layer first interprets the question, then determines the appropriate type of Earth observation analysis. The analytical pipeline then processes the images to produce actual results, such as regions of change or vegetation measurements. Only after the analysis produces evidence does the conversational layer generate an explanation of the results.
The system visualizes the findings, providing both textual explanation and graphical representation on the imagery or map. This separation allows the conversational component to communicate the findings without relying on the analytical pipeline for calculations or conclusions. The architecture supports following up on the conversation by remembering key points from previous interactions.
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