Deep neural networks (DNNs) are typically optimized for a specific input resolution (e.g. $224 \times 224$ px) and their adoption to inputs of higher resolution (e.g., satellite or medical images) remains challenging, as it leads to excessive computation and memory overhead, and may require substantial engineering effort (e.g., streaming). We show that multi