Remote sensing segmentation in real deployment is inherently continual: new semantic categories emerge, and acquisition conditions shift across seasons, cities, and sensors. Despite recent progress, many incremental approaches still treat training steps as isolated updates, which leaves representation drift and forgetting insufficiently controlled. We present ProtoFlow, a time-aware prototype dynamics framework that models class prototypes as trajectories and learns their evolution with an explicit temporal vector field. By jointly enforcing low-curvature motion and inter-class separation, ProtoFlow stabilizes prototype geometry throughout incremental learning. Experiments on standard class- and domain-incremental remote sensing benchmarks show consistent gains over strong baselines, including up to 1.5-2.0 points improvement in mIoUall, together with reduced forgetting. These results suggest that explicitly modeling temporal prototype evolution is a practical and interpretable strategy for robust continual remote sensing segmentation. Open-source code:https://github.com/dudududke/protoflow.

翻译：遥感分割在实际部署中本质上是连续的：新语义类别不断出现，采集条件随季节、城市和传感器而变化。尽管近期取得了进展，但许多增量方法仍将训练步骤视为孤立更新，导致对表示漂移和遗忘的控制不足。我们提出ProtoFlow，一种时间感知的原型动力学框架，将类原型建模为轨迹，并通过显式的时间向量场学习其演化。通过联合约束低曲率运动与类间分离，ProtoFlow在增量学习过程中稳定了原型几何结构。在标准类增量和域增量遥感基准上的实验表明，该方法在强基线基础上持续取得提升，包括mIoU_all指标提升1.5-2.0个百分点，同时遗忘减少。这些结果表明，显式建模时间原型演化是稳健连续遥感分割的一种实用且可解释的策略。开源代码：https://github.com/dudududke/protoflow。