The study of terrain and landform classification through UAV remote sensing diverges significantly from ground vehicle patrol tasks. Besides grappling with the complexity of data annotation and ensuring temporal consistency, it also confronts the scarcity of relevant data and the limitations imposed by the effective range of many technologies. This research substantiates that, in aerial positioning tasks, both the mean Intersection over Union (mIoU) and temporal consistency (TC) metrics are of paramount importance. It is demonstrated that fully labeled data is not the optimal choice, as selecting only key data lacks the enhancement in TC, leading to failures. Hence, a teacher-student architecture, coupled with key frame selection and key frame updating algorithms, is proposed. This framework successfully performs weakly supervised learning and TC knowledge distillation, overcoming the deficiencies of traditional TC training in aerial tasks. The experimental results reveal that our method utilizing merely 30\% of labeled data, concurrently elevates mIoU and temporal consistency ensuring stable localization of terrain objects. Result demo : https://gitlab.com/prophet.ai.inc/drone-based-riverbed-inspection

翻译：通过无人机遥感进行地形与地貌分类的研究，与地面车辆巡检任务存在显著差异。除了应对数据标注的复杂性及确保时序一致性外，还面临相关数据稀缺以及许多技术有效范围受限的挑战。本研究证实，在航拍定位任务中，平均交并比（mIoU）和时序一致性（TC）指标均至关重要。研究表明，全标注数据并非最优选择，因为仅选取关键数据缺乏对TC的提升，易导致任务失败。为此，本文提出一种结合关键帧选择与关键帧更新算法的师生架构。该框架成功实现了弱监督学习与TC知识蒸馏，克服了传统TC训练在航拍任务中的不足。实验结果表明，本方法仅使用30%的标注数据，即可同时提升mIoU与时序一致性，确保地形目标的稳定定位。结果演示：https://gitlab.com/prophet.ai.inc/drone-based-riverbed-inspection