Frontier models can’t run on satellites. Here’s an end-to-end wildfire detection pipeline using a 450M on-board Vision-Language Model (Sentinel-2 + LFM2.5-VL)

Sharing a project I've been building: a full end-to-end wildfire prevention pipeline that runs a Vision-Language Model directly on a satellite, using Sentinel-2 imagery.

The interesting design constraint isn't model quality. It's bandwidth. A frontier model on the ground means downlinking massive multispectral image matrices per orbit, which doesn't scale. A 450M VLM small enough to run on-board flips it: do inference in space, downlink only the JSON risk profile.

The pipeline pairs RGB (B4-B3-B2) with SWIR (B12-B8-B4) tiles. SWIR is the key signal. It captures vegetation moisture stress, which is the actual fuel indicator for fires. The VLM gets holistic scene understanding instead of just pixel stats, and outputs a structured risk_level plus breakdown.

For the PoC I'm simulating the on-board pipeline locally:

• SimSat (Docker) simulates orbit and serves real Sentinel-2 from the AWS Element84 STAC catalog
• LFM2.5-VL-450M runs locally via llama-server
• A watch loop polls position, fetches the image pair, runs inference, writes to SQLite
• Streamlit app on top to visualize predictions across 22 fire-prone locations (Attica, Angeles National Forest, Borneo, etc.)

This post covers problem framing and system design. The next ones cover data collection and labelling, evals, and fine-tuning, because out-of-the-box, a 450M VLM is not Opus-tier and you need to close that gap deliberately.

Code's in the Liquid AI Cookbook (link below). Curious what people think about on-device or on-edge inference for this kind of geospatial use case. Anyone doing similar work with constrained-bandwidth deployments?

Full write-up: https://github.com/Liquid4All/cookbook/tree/main/examples/wildfire-prevention

Code: https://github.com/Liquid4All/cookbook/tree/main/examples/wildfire-prevention