Automated architecture search can discover surprisingly effective small models for Earth observation tasks—in this case, a 1.6KB network that outperforms hand-designed baselines and fits on resource-constrained devices.
This paper uses evolutionary architecture search to automatically design neural networks for predicting chlorophyll-a levels in lakes from satellite imagery. Starting with a hand-designed model, the search finds smaller, better-performing networks (26× fewer parameters) that fit on edge devices for real-time monitoring.