With the rapid development of earth observation technology, we have entered an era of massively available satellite remote-sensing data. However, a large amount of satellite remote sensing data lacks a label or the label cost is too high to hinder the potential of AI technology mining satellite data. Especially in such an emergency response scenario that uses satellite data to evaluate the degree of disaster damage. Disaster damage assessment encountered bottlenecks due to excessive focus on the damage of a certain building in a specific geographical space or a certain area on a larger scale. In fact, in the early days of disaster emergency response, government departments were more concerned about the overall damage rate of the disaster area instead of single-building damage, because this helps the government decide the level of emergency response. We present an innovative algorithm that constructs Neyman stratified random sampling trees for binary classification and extends this approach to multiclass problems. Through extensive experimentation on various datasets and model structures, our findings demonstrate that our method surpasses both passive and conventional active learning techniques in terms of class rate estimation and model enhancement with only 30\%-60\% of the annotation cost of simple sampling. It effectively addresses the 'sampling bias' challenge in traditional active learning strategies and mitigates the 'cold start' dilemma. The efficacy of our approach is further substantiated through application to disaster evaluation tasks using Xview2 Satellite imagery, showcasing its practical utility in real-world contexts.

翻译：随着地球观测技术的飞速发展，我们已进入卫星遥感数据海量可用的时代。然而，大量卫星遥感数据缺乏标签或标注成本过高，阻碍了人工智能技术挖掘卫星数据潜力的进程。尤其是在利用卫星数据评估灾害损害程度的应急响应场景中。灾害损害评估因过度关注特定地理空间中某一建筑物的损毁情况或更大尺度上某一区域的损害而遭遇瓶颈。事实上，在灾害应急响应的早期阶段，政府部门更关注灾区的整体损毁率而非单体建筑损毁，因为这有助于政府确定应急响应级别。本文提出一种创新算法，该算法为二分类问题构建奈曼分层随机抽样树，并将此方法扩展至多分类问题。通过在多种数据集和模型结构上进行广泛实验，我们的研究结果表明，在仅需简单抽样标注成本30%-60%的情况下，本方法在类别率估计和模型增强方面均优于被动学习及传统主动学习技术。该方法有效解决了传统主动学习策略中的“抽样偏差”难题，并缓解了“冷启动”困境。通过将本方法应用于基于Xview2卫星影像的灾害评估任务，进一步验证了其有效性，展示了其在真实场景中的实际应用价值。