| --- |
| title: Agent4EO |
| emoji: 🛰️ |
| colorFrom: blue |
| colorTo: purple |
| sdk: gradio |
| sdk_version: 5.49.1 |
| app_file: app/main.py |
| pinned: false |
| --- |
| |
| ## Agent4EO |
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| 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. |
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| ## Challenge |
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| - **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](https://ai.google/earth-ai/), [Axion Planetary MCP](https://github.com/Dhenenjay/axion-planetary-mcp), [Ageospatial](https://ageospatial.com/), [GeoRetina](https://www.georetina.com/) |
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| ## Solution |
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| - 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. |
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| ## Results |
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| - 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. |
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| ## Possible extensions |
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| - 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. |
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