GaiaBot

LLM-assisted environmental data interface for Earth observation, soil, and field-context information with traceable references.

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Conversational interface linking EO-derived soil indicators with field context to recommend fertilization strategies.
AI assistant explaining soil carbon and clay content using map-based EO data to support irrigation and soil management decisions.
Oral presentation showcasing an AI-driven interface for simplifying access to EO-derived soil data at the IEEE IGARSS conference in Brisbane, Australia.
LLMsRAGEarth ObservationDecision SupportPython
AI & LLM · Agriculture
2024–Present
University of Florida · Soil AI Lab

GaiaBot

GaiaBot is an LLM-assisted environmental data interface designed to simplify access to Earth observation, soil, and field-context information while keeping responses grounded in traceable data sources and technical references.

GaiaBot is an LLM-assisted environmental data interface designed to simplify access to Earth observation, soil, and field-context information while keeping responses grounded in traceable data sources and technical references.

Question interpretation, retrieval of relevant environmental layers or references, and retrieval-augmented responses that connect technical data with practical agricultural and soil-management questions.

Soil monitoring summaries, irrigation and nutrient-management context, post-event damage assessment support, and explanation of map-based indicators for non-specialist users.

Key Features

  • Simplifies access to EO and environmental datasets
  • Turns complex indicators into practical recommendations
  • Designed for transparency and traceable sources
  • Built to integrate with dashboards and field workflows