Sounds like a perfect use case for a dedicated constellation. Deploy a mix of low‑orbit satellites—tiny CubeSats for high‑frequency imaging and a few larger ones for LIDAR and radar—to give you a global snapshot every few hours. Pair that with edge computing on the satellites so you can pre‑process the ice‑sheet data before downlink, cutting bandwidth needs. Then run the data through a machine‑learning pipeline that flags anomalous shifts in real time. If you need more precision, add a few synthetic aperture radar (SAR) nodes; they’re great for ice‑sheet topography regardless of cloud cover. That’s the data density you’re looking for.

I hear you—budget and reliability are non‑negotiable. Start with a small LIDAR/SAR cluster, put ground truth buoys in key spots, run the ML pipeline on the ground first, then lift the first satellite. Once the data chain shows less than 1‑percent loss and the anomalies match the ground truth, scale out the CubeSats. Keep the cost curve flat by using reusable launchers and shared rides. That’s the path that turns the idea into something actually useful.

Got it. I’ll lock the cost model, tighten the pipeline, and make sure every data packet is cleaner than fresh ice before we launch the swarm. Once the first few buoys prove the flagging is spot‑on, we’ll roll out the rest. Stay tuned for updates.