README.md

metadata

title: Agent4EO
emoji: 🛰️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app/main.py
pinned: false

Agent4EO

Agent4EO is a demo that investigates and demystifies geospatial reasoning by using a lightweight agent to turn curated satellite samples into clear, operational insights with concise narratives.

---

Challenge

• Decision-makers don’t need raw imagery; they need clear, operational signals, still many Earth Observation (EO) use-cases fail to cross the gap from geospatial experts to end-users, from research to deployment.
• Staying current with a fast-moving field while making geospatial reasoning explicit : As a curiosity-driven AI Research Engineer, I had to investigate agentic workflows and the pipeline structures behind groundbreaking initiatives capturing attention: Google Earth AI, Axion Planetary MCP, Ageospatial, GeoRetina

Solution

• Curated samples are fed to a routing agent that selects the appropriate analysis, like a geospatial expert would.
• Implemented tools: Vegetation condition analysis (NDVI), Urban Heat Island monitoring (LST), and burn scars charcterization (dNBR).
• OpenStreetMap context (API call) is attached to computed metrics to anchor results to places.
• A second LLM pass turns numbers + context into a concise operational narrative.
• The interface displays a quicklook, histogram, quantitative metrics, and a short, number-grounded explanation.
• The agent is powered by Ministral-3B for efficiency; orchestration uses LangChain. EO I/O uses Rasterio with proper CRS/transform and not-relevant data handling.

Results

• A live, accessible project : I believe in public demos over private perfection.
• Clear visual and textual outputs understandable by non-experts, revealing Earth Observation's added value.
• A concrete exploration of agentic orchestration for EO that other practitioners can reuse as a reference pattern.

Possible extensions

• Move to true prompt-driven analysis: users specify topic, period, and region; the agent fetches data on demand (from a way larger range of sources) and selects tools accordingly.
• Richer narratives tailored to roles (territorial planning, insurers...) with domain-aware capacity.
• Expand the toolset: multi-temporal change maps, flood risk, landslide tracking, building characterization, oil-spill detection, SAR flood mapping, and embeddings-based heads with integration of Foundation Models
• Enable on-the-fly tool drafting (code synthesis) when safe and auditable.
• Be deliberate with LLMs: for such a small range of tools, selection via LLM isn’t necessary, and with such low context models can hallucinate. Agents should be used where they add real leverage, beyond conventional programming capacities.