On‑Board Satellite VLM for Wildfire Detection
Build an on‑board satellite VLM pipeline that processes Sentinel‑2 images and outputs risk profiles.
Prototype the pipeline on local hardware and evaluate inference latency.
Summary
A project demonstrates an end‑to‑end wildfire prevention pipeline that runs a 450M Vision‑Language Model (LFM2.5‑VL‑450M) directly on a satellite using Sentinel‑2 imagery. The design prioritises bandwidth: the VLM processes RGB (B4‑B3‑B2) and SWIR (B12‑B8‑B4) tiles locally, then downlinks only a JSON risk profile instead of large image matrices.
The pipeline uses SimSat Docker to simulate orbit and serve real Sentinel‑2 data from the AWS Element84 STAC catalog. The VLM is run via llama‑server, and a watch loop polls the satellite position, fetches the image pair, runs inference, and writes results to SQLite. A Streamlit app visualises predictions across 22 fire‑prone locations.
The post outlines the system design and mentions future work on data collection, fine‑tuning, and evaluation. The code is available in the Liquid AI Cookbook.
This approach showcases how constrained‑bandwidth deployments can still leverage large models for critical geospatial tasks.
Key changes
- Uses 450M Vision‑Language Model (LFM2.5‑VL‑450M)
- Simulates orbit with SimSat Docker
- Processes RGB and SWIR tiles
- Outputs structured risk_level JSON
- Stores results in SQLite
- Visualises via Streamlit