[2603.03075] TinyIceNet: Low-Power SAR Sea Ice Segmentation for On-Board FPGA Inference

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.03075 (cs) [Submitted on 3 Mar 2026] Title:TinyIceNet: Low-Power SAR Sea Ice Segmentation for On-Board FPGA Inference Authors:Mhd Rashed Al Koutayni, Mohamed Selim, Gerd Reis, Alain Pagani, Didier Stricker View a PDF of the paper titled TinyIceNet: Low-Power SAR Sea Ice Segmentation for On-Board FPGA Inference, by Mhd Rashed Al Koutayni and 4 other authors View PDF HTML (experimental) Abstract:Accurate sea ice mapping is essential for safe maritime navigation in polar regions, where rapidly changing ice conditions require timely and reliable information. While Sentinel-1 Synthetic Aperture Radar (SAR) provides high-resolution, all-weather observations of sea ice, conventional ground-based processing is limited by downlink bandwidth, latency, and energy costs associated with transmitting large volumes of raw data. On-board processing, enabled by dedicated inference chips integrated directly within the satellite payload, offers a transformative alternative by generating actionable sea ice products in orbit. In this context, we present TinyIceNet, a compact semantic segmentation network co-designed for on-board Stage of Development (SOD) mapping from dual-polarized Sentinel-1 SAR imagery under strict hardware and power constraints. Trained on the AI4Arctic dataset, TinyIceNet combines SAR-aware architectural simplifications with low-precision quantization to balance accuracy and efficiency. The model is syn...